RESUMO
Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel method to enhance the explainability of transformer-based image classification models. Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps. We introduce two modules: the "Relationship Weighted Out" and the "Cut" modules. The "Relationship Weighted Out" module focuses on extracting class-specific information from intermediate layers, enabling us to highlight relevant features. Additionally, the "Cut" module performs fine-grained feature decomposition, taking into account factors such as position, texture, and color. By integrating these modules, we generate dense class-specific visual explainability maps. We validate our method with extensive qualitative and quantitative experiments on the ImageNet dataset. Furthermore, we conduct a large number of experiments on the LRN dataset, which is specifically designed for automatic driving danger alerts, to evaluate the explainability of our method in scenarios with complex backgrounds. The results demonstrate a significant improvement over previous methods. Moreover, we conduct ablation experiments to validate the effectiveness of each module. Through these experiments, we are able to confirm the respective contributions of each module, thus solidifying the overall effectiveness of our proposed approach.
RESUMO
BACKGROUND: There is a need for effective and early functional rehabilitation of patients with gait and balance problems including those with spinal cord injury, neurological diseases and recovering from hip fractures, a common consequence of falls especially in the elderly population. Gait training in these patients using partial body weight support (BWS) on a treadmill, a technique that involves unloading the subject through a harness, improves walking better than training with full weight bearing. One problem with this technique not commonly acknowledged is that the harness provides external support that essentially eliminates associated postural adjustments (APAs) required for independent gait. We have developed a device to address this issue and conducted a training study for proof of concept of efficacy. METHODS: We present a tool that can enhance the concept of BWS training by allowing natural APAs to occur mediolaterally. While in a supine position in a 90 deg tilted environment built around a modified hospital bed, subjects wear a backpack frame that is freely moving on air-bearings (cf. puck on an air hockey table) and attached through a cable to a pneumatic cylinder that provides a load that can be set to emulate various G-like loads. Veridical visual input is provided through two 3-D automultiscopic displays that allow glasses free 3-D vision representing a virtual surrounding environment that may be acquired from sites chosen by the patient. Two groups of 12 healthy subjects were exposed to either strength training alone or a combination of strength and balance training in such a tilted environment over a period of four weeks. RESULTS: Isokinetic strength measured during upright squat extension improved similarly in both groups. Measures of balance assessed in upright showed statistically significant improvements only when balance was part of the training in the tilted environment. Postural measures indicated less reliance on visual and/or increased use of somatosensory cues after training. CONCLUSION: Upright balance function can be improved following balance specific training performed in a supine position in an environment providing the perception of an upright position with respect to gravity. Future studies will implement this concept in patients.
