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Found 12 papers, 1 papers with code
DeconfuseTrack:Dealing with Confusion for Multi-Object Tracking
Moreover, DeconfuseTrack achieves state-of-the-art performance on the MOT17 and MOT20 test sets, significantly outperforms the baseline tracker ByteTrack in metrics such as HOTA, IDF1, AssA.
Focus On Details: Online Multi-object Tracking with Diverse Fine-grained Representation
This fine-grained representation requires high feature resolution and precise semantic information.
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation
VLM joints the information in the generated visual prompts and the textual prompts from a pre-defined Trackbook to obtain instance-level pseudo textual description, which is domain invariant to different tracking scenes.
Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking
To this end, we propose a strategy, namely multi-view trajectory contrastive learning, in which each trajectory is represented as a center vector.
Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Method
Through extensive experiments, AI-GAN achieves high attack success rates, outperforming existing methods, and reduces generation time significantly.
RelationTrack: Relation-aware Multiple Object Tracking with Decoupled Representation
Existing online multiple object tracking (MOT) algorithms often consist of two subtasks, detection and re-identification (ReID).
ANL: Anti-Noise Learning for Cross-Domain Person Re-Identification
Usually, attributed to the domain gaps, the pre-trained source domain model cannot extract appropriate target domain features, which will dramatically affect the clustering performance and the accuracy of pseudo-labels.
FTN: Foreground-Guided Texture-Focused Person Re-Identification
Person re-identification (Re-ID) is a challenging task as persons are often in different backgrounds.
MAT: Motion-Aware Multi-Object Tracking
Modern multi-object tracking (MOT) systems usually model the trajectories by associating per-frame detections.
Refinements in Motion and Appearance for Online Multi-Object Tracking
Modern multi-object tracking (MOT) system usually involves separated modules, such as motion model for location and appearance model for data association.
Poisson Kernel Avoiding Self-Smoothing in Graph Convolutional Networks
Graph convolutional network (GCN) is now an effective tool to deal with non-Euclidean data, such as social networks in social behavior analysis, molecular structure analysis in the field of chemistry, and skeleton-based action recognition.
AI-GAN: Attack-Inspired Generation of Adversarial Examples
Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs.
