Generative Partial Visual-Tactile Fused Object Clustering
Authors: Tao Zhang, Yang Cong, Gan Sun, Jiahua Dong, Yuyang Liu, Zhengming Ding6156-6164
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive comparative experiments on three public visual-tactile datasets prove the effectiveness of our method. |
| Researcher Affiliation | Academia | 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences 2Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences 3University of Chinese Academy of Sciences 4Department of Computer Science, Tulane University |
| Pseudocode | Yes | Algorithm 1 Training Process of the Proposed Framework |
| Open Source Code | No | The paper states 'We implement the model with Tensorflow 1.12.0', but does not provide any concrete access (link or explicit statement of release) to the source code for the described methodology. |
| Open Datasets | Yes | PHAC-2 (Gao et al. 2016) dataset consists of color images and tactile signals of 53 household objects... LMT (Zheng et al. 2016; Strese, Schuwerk, and Steinbach 2015) dataset consists of 10 color images and 30 haptic acceleration data of 108 different surface materials... Gel Fabric (Yuan et al. 2017) dataset includes visual data (i.e., color and depth images) and tactile data of 119 kind of different fabrics. |
| Dataset Splits | No | The paper defines how partial data is generated but does not explicitly provide training/validation/test dataset splits (e.g., percentages or counts for each set) or reference standard predefined splits for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | Yes | We implement the model with Tensorflow 1.12.0 |
| Experiment Setup | Yes | We implement the model with Tensorflow 1.12.0, and set the batch size to be 64... the learning rates are set to be 0.0001... the learning rates are set to be 0.000003 and 0.000004 for G1( ) and G2( ), respectively. Next, the two discriminators D1( ) and D2( ) are optimized by Eq. (8) with Adam optimizers and the leaning rates are set to be 0.000001 both for D1( ) and D2( ). We update the generators five times while updating the discriminators once... when β is set to be 1, we gain the best performance. Thus we empirically choose β = 1 as default in this paper... our proposed GPVTF model performs best when ϕ1 and ϕ2 are set to be 0.01. Thus, we empirically choose β = 1, ϕ1 = 0.01 and ϕ2 = 0.01 as default in this paper in order to achieve the best performance. |