HitBack: Transformer With Hierarchical-Semantic Cross Attention and Background Contrast for Weakly Supervised Wildlife Semantic Segmentation

Monitoring wildlife behavior and population changes is critical for conservation efforts. However, specialized analysis of large volumes of wildlife images is extremely challenging, necessitating the use of artificial intelligence techniques to automatically detect, segment, and classify species captured by trap cameras. Despite the increasing use of AI in wildlife monitoring, challenges with data quality and availability persist. The Snapshot Serengeti (SS) dataset only has image-level labels and very few bounding box labels, and there's no dataset with pixel-level labels due to the significant annotation costs. To this end, we create and release the large-scale Semantic Segmentation for Snapshots of the Serengeti (S4) dataset, consisting of 24K high-quality images across 47 species with precise masks, for both common and rare species. This dataset serves as a resource for developing semantic segmentation algorithms in wildlife studies. Additionally, we introduce HitBack, a novel method leveraging Hierarchical-Semantic Cross Attention (HCA) and Background Contrast (BC) for weakly supervised semantic segmentation (WSSS). The HCA module is used to capture both the shared and distinct features across species, and the BC module is designed to enhance foreground activation by ensuring consistency in the backgrounds. Extensive experiments on the newly proposed (S4) benchmark show that, our HitBack presents competitive performance when compared with the state-of-the-art models. The mIoU of HitBack is +10.4%, +14.7%, and +18.4% higher than that of ToCo, SIPE, and MCTformer, respectively. In addition, our HitBack even obtains performance that surpasses the fully-supervised and semi-supervised methods when annotation data is limited. Code and datasets will be available at Github.