How is Visual Attention Influenced by Text Guidance? Database and Model

The analysis and prediction of visual attention have long been crucial tasks in the fields of computer vision and image processing. In practical applications, images are generally accompanied by various text descriptions, however, few studies have explored the influence of text descriptions on visual attention, let alone developed visual saliency prediction models considering text guidance. In this paper, we conduct a comprehensive study on text-guided image saliency (TIS) from both subjective and objective perspectives. Specifically, we construct a TIS database named SJTU-TIS, which includes 1200 text-image pairs and the corresponding collected eye-tracking data. Based on the established SJTU-TIS database, we analyze the influence of various text descriptions on visual attention. Then, to facilitate the development of saliency prediction models considering text influence, we construct a benchmark for the established SJTU-TIS database using state-of-the-art saliency models. Finally, considering the effect of text descriptions on visual attention, while most existing saliency models ignore this impact, we further propose a text-guided saliency (TGSal) prediction model, which extracts and integrates both image features and text features to predict the image saliency under various text-description conditions. Our proposed model significantly outperforms the state-of-the-art saliency models on both the SJTU-TIS database and the pure image saliency databases in terms of various evaluation metrics. The SJTU-TIS database and the code of the proposed TGSal model will be released at: https://github.com/IntMeGroup/TGSal.

TikTokActions: A TikTok-Derived Video Dataset for Human Action Recognition

The increasing variety and quantity of tagged multimedia content on platforms such as TikTok provides an opportunity to advance computer vision modeling. We have curated a distinctive dataset of 283,582 unique video clips categorized under 386 hashtags relating to modern human actions. We release this dataset as a valuable resource for building domain-specific foundation models for human movement modeling tasks such as action recognition. To validate this dataset, which we name TikTokActions, we perform two sets of experiments. First, we pretrain the state-of-the-art VideoMAEv2 with a ViT-base backbone on TikTokActions subset, and then fine-tune and evaluate on popular datasets such as UCF101 and the HMDB51. We find that the performance of the model pre-trained using our Tik-Tok dataset is comparable to models trained on larger action recognition datasets (95.3% on UCF101 and 53.24% on HMDB51). Furthermore, our investigation into the relationship between pre-training dataset size and fine-tuning performance reveals that beyond a certain threshold, the incremental benefit of larger training sets diminishes. This work introduces a useful TikTok video dataset that is available for public use and provides insights into the marginal benefit of increasing pre-training dataset sizes for video-based foundation models.

Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot

We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Developing algorithms to enable a legged robot to shoot a soccer ball to a given target is a challenging problem that combines robot motion control and planning into one task. To solve this problem, we need to consider the dynamics limitation and motion stability during the control of a dynamic legged robot. Moreover, we need to consider motion planning to shoot the hard-to-model deformable ball rolling on the ground with uncertain friction to a desired location. In this paper, we propose a hierarchical framework that leverages deep reinforcement learning to train (a) a robust motion control policy that can track arbitrary motions and (b) a planning policy to decide the desired kicking motion to shoot a soccer ball to a target. We deploy the proposed framework on an A1 quadrupedal robot and enable it to accurately shoot the ball to random targets in the real world.

A Validated Physical Model For Real-Time Simulation of Soft Robotic Snakes

In this work we present a framework that is capable of accurately representing soft robotic actuators in a multiphysics environment in real-time. We propose a constraint-based dynamics model of a 1-dimensional pneumatic soft actuator that accounts for internal pressure forces, as well as the effect of actuator latency and damping under inflation and deflation and demonstrate its accuracy a full soft robotic snake with the composition of multiple 1D actuators. We verify our model's accuracy in static deformation and dynamic locomotion open-loop control experiments. To achieve real-time performance we leverage the parallel computation power of GPUs to allow interactive control and feedback.