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Found 15 papers, 9 papers with code
Unified and Dynamic Graph for Temporal Character Grouping in Long Videos
In this paper, we present a unified and dynamic graph (UniDG) framework for temporal character grouping.
D3G: Exploring Gaussian Prior for Temporal Sentence Grounding with Glance Annotation
Under this setup, we propose a Dynamic Gaussian prior based Grounding framework with Glance annotation (D3G), which consists of a Semantic Alignment Group Contrastive Learning module (SA-GCL) and a Dynamic Gaussian prior Adjustment module (DGA).
Ranked #10 on
Temporal Sentence Grounding
on Charades-STA
Collaborative Noisy Label Cleaner: Learning Scene-aware Trailers for Multi-modal Highlight Detection in Movies
Based on existing efforts, this work has two observations: (1) For different annotators, labeling highlight has uncertainty, which leads to inaccurate and time-consuming annotations.
NewsNet: A Novel Dataset for Hierarchical Temporal Segmentation
Temporal video segmentation is the get-to-go automatic video analysis, which decomposes a long-form video into smaller components for the following-up understanding tasks.
VLMAE: Vision-Language Masked Autoencoder
Image and language modeling is of crucial importance for vision-language pre-training (VLP), which aims to learn multi-modal representations from large-scale paired image-text data.
See Finer, See More: Implicit Modality Alignment for Text-based Person Retrieval
To explore the fine-grained alignment, we further propose two implicit semantic alignment paradigms: multi-level alignment (MLA) and bidirectional mask modeling (BMM).
Exploiting Feature Diversity for Make-up Temporal Video Grounding
This technical report presents the 3rd winning solution for MTVG, a new task introduced in the 4-th Person in Context (PIC) Challenge at ACM MM 2022.
Head and Body: Unified Detector and Graph Network for Person Search in Media
It is by far the largest dataset for person search in media.
Learning to Disentangle Scenes for Person Re-identification
In this way, the complex scenes in the ReID task are effectively disentangled, and the burden of each branch is relieved.
Ranked #2 on
Person Re-Identification
on P-DukeMTMC-reID
Exploiting Robust Unsupervised Video Person Re-identification
A local-aware module is employed to explore the poentials of local-level feature for unsupervised learning.
Ranked #1 on
Unsupervised Person Re-Identification
on PRID2011
Unsupervised Person Re-Identification
Video-Based Person Re-Identification
Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks
Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific.
Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification
The key to this task is to exploit cloth-irrelevant cues.
MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking
The visible and thermal filters will be used to conduct a dynamic convolutional operation on their corresponding input feature maps respectively.
Ranked #21 on
Rgb-T Tracking
on RGBT234
Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmark
We also verified that models pre-trained on LaST can generalize well on existing datasets with short-term and cloth-changing scenarios.
Towards More Flexible and Accurate Object Tracking with Natural Language: Algorithms and Benchmark
We believe this benchmark will greatly boost related researches on natural language guided tracking.
Ranked #3 on
Visual Object Tracking
on TNL2K
(precision metric)
