With the emergence of large pre-trained multimodal video models, multiple benchmarks have been proposed to evaluate model capabilities. However, most of the benchmarks are human-centric, with evaluation data and tasks centered around human applications. Animals are an integral part of the natural world, and animal-centric video understanding is crucial for animal welfare and conservation efforts. Yet, existing benchmarks overlook evaluations focused on animals, limiting the application of the models. To address this limitation, our work established an animal-centric benchmark, namely Animal-Bench, to allow for a comprehensive evaluation of model capabilities in real-world contexts, overcoming agent-bias in previous benchmarks. Animal-Bench includes 13 tasks encompassing both common tasks shared with humans and special tasks relevant to animal conservation, spanning 7 major animal categories and 819 species, comprising a total of 41,839 data entries. To generate this benchmark, we defined a task system centered on animals and proposed an automated pipeline for animal-centric data processing. To further validate the robustness of models against real-world challenges, we utilized a video editing approach to simulate realistic scenarios like weather changes and shooting parameters due to animal movements. We evaluated 8 current multimodal video models on our benchmark and found considerable room for improvement. We hope our work provides insights for the community and opens up new avenues for research in multimodal video models. Our data and code will be released at https://github.com/PRIS-CV/Animal-Bench.

Animal action recognition has a wide range of applications. However, the field largely remains unexplored due to the greater challenges compared to human action recognition, such as lack of annotated training data, large intra-class variation, and interference of cluttered background. Most of the existing methods directly apply human action recognition techniques, which essentially require a large amount of annotated data. In recent years, contrastive vision-language pretraining has demonstrated strong zero-shot generalization ability and has been used for human action recognition. Inspired by the success, we develop a highly performant action recognition framework based on the CLIP model. Our model addresses the above challenges via a novel category-specific prompting module to generate adaptive prompts for both text and video based on the animal category detected in input videos. On one hand, it can generate more precise and customized textual descriptions for each action and animal category pair, being helpful in the alignment of textual and visual space. On the other hand, it allows the model to focus on video features of the target animal in the video and reduce the interference of video background noise. Experimental results demonstrate that our method outperforms five previous action recognition methods on the Animal Kingdom dataset and has shown best generalization ability on unseen animals.

In the field of road extraction, an dominant network is D-LinkNet which won the first place in DeepGlobe 2018 challenge. Although D-LinkNet creatively proposed D-block with progressively enlarged dilated convolution and proved its efficiency, the$\mathrm{N}\times \mathrm{N}$square kernel it used still has limitations for road extraction. Road in aerial imagery usually has narrow-and-long shape, and the direction is randomly distributed. Therefore, not only large receptive field but also anisotropic long-range contextual information should be considered. Based on this intuition, we integrate a new pooling strategy named strip pooling which uses a long but narrow kernel i.e.$1\times \mathrm{N}$or$\mathrm{N}\times 1$into D-LinkNet. With strip pooling module(SPM) and mixed pooling module(MPM) designed based on strip pooling, two modifications are made to D-LinkNet: 1) We insert SPM into Res-block of the original encoder ResNet34 and name it Res-SPM-block. 2) Inspired by MPM, we connect strip pooling in parallel with D-block and name it SPD-block. We name the upgraded D-LinkNet as SPD-LinkNet. Experimental results on DeepGlobe 2018 dataset prove that SPD-LinkNet outperforms original D-LinkNet in accuracy while maintaining nearly the same inference speed.