# Adversarial Bipartite Graph Learning for Video Domain Adaptation

arXiv: https://arxiv.org/abs/2007.15829 · HTML: https://mlsanity.com/paper/2007.15829/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 28 of 28)

- [Simple Contrastive Graph Clustering](https://mlsanity.com/paper/2205.07865/) (2022)
- [Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing](https://mlsanity.com/paper/2110.15128/) (2021)
- [In Search of Lost Online Test-time Adaptation: A Survey](https://mlsanity.com/paper/2310.20199/) (2023)
- [Contrastive Learning for Unsupervised Domain Adaptation of Time Series](https://mlsanity.com/paper/2206.06243/) (2022)
- [Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling](https://mlsanity.com/paper/2307.07944/) (2023)
- [Learning Bounds for Open-Set Learning](https://mlsanity.com/paper/2106.15792/) (2021)
- [Source-Free Progressive Graph Learning for Open-Set Domain Adaptation](https://mlsanity.com/paper/2202.06174/) (2022)
- [Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective](https://mlsanity.com/paper/2208.07365/) (2022)
- [Diversifying Spatial-Temporal Perception for Video Domain Generalization](https://mlsanity.com/paper/2310.17942/) (2023)
- [AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation](https://mlsanity.com/paper/2304.01110/) (2023)
- [Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection](https://mlsanity.com/paper/2206.02355/) (2022)
- [XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition](https://mlsanity.com/paper/2403.01560/) (2024)
- [Mitigating Generation Shifts for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2107.03163/) (2021)
- [Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2211.10412/) (2022)
- [ALEX: Towards Effective Graph Transfer Learning with Noisy Labels](https://mlsanity.com/paper/2309.14673/) (2023)
- [Conditional Extreme Value Theory for Open Set Video Domain Adaptation](https://mlsanity.com/paper/2109.00522/) (2021)
- [Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2101.03292/) (2021)
- [Human-Centric Transformer for Domain Adaptive Action Recognition](https://mlsanity.com/paper/2407.10860/) (2024)
- [CycDA: Unsupervised Cycle Domain Adaptation from Image to Video](https://mlsanity.com/paper/2203.16244/) (2022)
- [Discovering Domain Disentanglement for Generalized Multi-source Domain Adaptation](https://mlsanity.com/paper/2207.05070/) (2022)
- [Jacobian Norm for Unsupervised Source-Free Domain Adaptation](https://mlsanity.com/paper/2204.03467/) (2022)
- [Generalizable Prompt Learning of CLIP: A Brief Overview](https://mlsanity.com/paper/2503.01263/) (2025)
- [Attribute-aware Explainable Complementary Clothing Recommendation](https://mlsanity.com/paper/2107.01655/) (2021)
- [SAR-to-Optical Image Translation via Thermodynamics-inspired Network](https://mlsanity.com/paper/2305.13839/) (2023)
- [Time Series Domain Adaptation via Latent Invariant Causal Mechanism](https://mlsanity.com/paper/2502.16637/) (2025)
- [Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning](https://mlsanity.com/paper/2206.12592/) (2022)
- [Memory Efficient Temporal & Visual Graph Model for Unsupervised Video Domain Adaptation](https://mlsanity.com/paper/2208.06554/) (2022)
- [Return of Frustratingly Easy Unsupervised Video Domain Adaptation](https://mlsanity.com/paper/2605.19510/) (2026)

## Abstract (arXiv)

Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area due to the significant spatial and temporal shifts across the source (i.e. training) and target (i.e. test) domains. As such, recent works on visual domain adaptation which leverage adversarial learning to unify the source and target video representations and strengthen the feature transferability are not highly effective on the videos. To overcome this limitation, in this paper, we learn a domain-agnostic video classifier instead of learning domain-invariant representations, and propose an Adversarial Bipartite Graph (ABG) learning framework which directly models the source-target interactions with a network topology of the bipartite graph. Specifically, the source and target frames are sampled as heterogeneous vertexes while the edges connecting two types of nodes measure the affinity among them. Through message-passing, each vertex aggregates the features from its heterogeneous neighbors, forcing the features coming from the same class to be mixed evenly. Explicitly exposing the video classifier to such cross-domain representations at the training and test stages makes our model less biased to the labeled source data, which in-turn results in achieving a better generalization on the target domain. To further enhance the model capacity and testify the robustness of the proposed architecture on difficult transfer tasks, we extend our model to work in a semi-supervised setting using an additional video-level bipartite graph. Extensive experiments conducted on four benchmarks evidence the effectiveness of the proposed approach over the SOTA methods on the task of video recognition.