Search Results for author:
Found 12 papers, 5 papers with code
Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking
In this paper, we design experiments to verify such misfit between global re-ID feature distances and local matching in tracking, and propose a simple yet effective approach to adapt affinity estimations to corresponding matching scopes in MTMCT.
Action Units That Constitute Trainable Micro-expressions (and A Large-scale Synthetic Dataset)
Because of the expensive data collection process, micro-expression (MiE) datasets are generally much smaller in scale than those in other computer vision fields, rendering large-scale training less feasible.
Do Different Tracking Tasks Require Different Appearance Models?
We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered.
Ranked #1 on
Video Object Segmentation
on DAVIS 2017
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+10
Synthetic Data Are as Good as the Real for Association Knowledge Learning in Multi-object Tracking
We show that compared with real data, association knowledge obtained from synthetic data can achieve very similar performance on real-world test sets without domain adaption techniques.
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.
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
(dataset metric)
Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking
Due to the continuity of target trajectories, tracking systems usually restrict their data association within a local neighborhood.
Towards Real-Time Multi-Object Tracking
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
Ranked #12 on
Multi-Object Tracking
on MOT16
(using extra training data)
Softmax Dissection: Towards Understanding Intra- and Inter-class Objective for Embedding Learning
The softmax loss and its variants are widely used as objectives for embedding learning, especially in applications like face recognition.
Linkage Based Face Clustering via Graph Convolution Network
The key idea is that we find the local context in the feature space around an instance (face) contains rich information about the linkage relationship between this instance and its neighbors.
Query Adaptive Late Fusion for Image Retrieval
That is to say, for some queries, a feature may be neither discriminative nor complementary to existing ones, while for other queries, the feature suffices.
Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification
In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed.
