Adversarial Bipartite Graph Learning for Video Domain Adaptation

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.Comment: Proceedings of the 28th ACM International Conference on Multimedia (MM '20

Alleviating Human-level Shift : A Robust Domain Adaptation Method for Multi-person Pose Estimation

Human pose estimation has been widely studied with much focus on supervised learning requiring sufficient annotations. However, in real applications, a pretrained pose estimation model usually need be adapted to a novel domain with no labels or sparse labels. Such domain adaptation for 2D pose estimation hasn't been explored. The main reason is that a pose, by nature, has typical topological structure and needs fine-grained features in local keypoints. While existing adaptation methods do not consider topological structure of object-of-interest and they align the whole images coarsely. Therefore, we propose a novel domain adaptation method for multi-person pose estimation to conduct the human-level topological structure alignment and fine-grained feature alignment. Our method consists of three modules: Cross-Attentive Feature Alignment (CAFA), Intra-domain Structure Adaptation (ISA) and Inter-domain Human-Topology Alignment (IHTA) module. The CAFA adopts a bidirectional spatial attention module (BSAM)that focuses on fine-grained local feature correlation between two humans to adaptively aggregate consistent features for adaptation. We adopt ISA only in semi-supervised domain adaptation (SSDA) to exploit the corresponding keypoint semantic relationship for reducing the intra-domain bias. Most importantly, we propose an IHTA to learn more domain-invariant human topological representation for reducing the inter-domain discrepancy. We model the human topological structure via the graph convolution network (GCN), by passing messages on which, high-order relations can be considered. This structure preserving alignment based on GCN is beneficial to the occluded or extreme pose inference. Extensive experiments are conducted on two popular benchmarks and results demonstrate the competency of our method compared with existing supervised approaches.Comment: Accepted By ACM MM'202

The Slender-billed Curlew Numenius tenuirostris in Africa

Volume: 17Start Page: 202End Page: 20

The status of Gurney's Pitta <i>Pitta gumeyi</i>, 1987–1989

SummaryFieldwork aimed at censusing Gumey's Pitta Pitta gumeyi in Peninsular Thailand was carried out over three field seasons. Fourteen sites were surveyed, at four of which the species was found. The main site (where it had been rediscovered in 1986) held 24–34 pairs, 12–18 of which were in the 500 ha study area. A second site held 3-6 pairs (but it is thought unlikely that this population still exists today), whilst the other two sites held only two pairs each and were thought to have negligible chances of survival. All territories were in semi-evergreen rainforest, below 150 m altitude. The current population i s probably some 20–30 pairs, with territories still being lost annually to deforestation. This is currently the total known world population; it is possible that the species may survive in southern Burma, but no recent surveys have been undertaken there. Furthermore, massive deforestation caused by Thai timber companies has been reported from Burma during 1988–1993. The interpretation of census results are discussed, particularly with reference to social organization and calling seasonality. The determined protection of the one remaining site supporting a viable population will be essential if the species is to survive into the next century.</jats:p

Survey of waterbirds wintering in Tunisia, January 2003

During a joint international expedition, there have been recorded between 18-31 January 2003 a total number of 83,653 water birds(belonging to 76 species) in 58 wetlands of Tunisia