Unbiased Scene Graph Generation via Two-stage Causal Modeling

Despite the impressive performance of recent unbiased Scene Graph Generation (SGG) methods, the current debiasing literature mainly focuses on the long-tailed distribution problem, whereas it overlooks another source of bias, i.e., semantic confusion, which makes the SGG model prone to yield false predictions for similar relationships. In this paper, we explore a debiasing procedure for the SGG task leveraging causal inference. Our central insight is that the Sparse Mechanism Shift (SMS) in causality allows independent intervention on multiple biases, thereby potentially preserving head category performance while pursuing the prediction of high-informative tail relationships. However, the noisy datasets lead to unobserved confounders for the SGG task, and thus the constructed causal models are always causal-insufficient to benefit from SMS. To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages. The first stage is causal representation learning, where we use a novel Population Loss (P-Loss) to intervene in the semantic confusion confounder. The second stage introduces the Adaptive Logit Adjustment (AL-Adjustment) to eliminate the long-tailed distribution confounder to complete causal calibration learning. These two stages are model agnostic and thus can be used in any SGG model that seeks unbiased predictions. Comprehensive experiments conducted on the popular SGG backbones and benchmarks show that our TsCM can achieve state-of-the-art performance in terms of mean recall rate. Furthermore, TsCM can maintain a higher recall rate than other debiasing methods, which indicates that our method can achieve a better tradeoff between head and tail relationships.Comment: 17 pages, 9 figures. Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligenc

Heat transfer performance in 3D internally finned heat pipe

An experimental study of heat transfer performance in 3D internally finned steel-water heat pipe was carried out in this project. All the main parameters that can significantly influence the heat transfer performance of heat pipe, such as working temperature, heat flux, inclination angle, working fluid fill ratio (defined by the evaporation volume), have been examined. Within the experimental conditions (working temperature 40 C–95 C, heat flux 5.0 kw/m2–40 kw/m2, inclination angle 2–90 ), the evaporation and condensation heat transfer coefficients in 3D internally finned heat pipe are found to be increased by 50–100% and 100–200%, respectively, as compared to the smooth gravity-assisted heat pipe under the same conditions. Therefore, it is concluded that the special structures of 3D-fins on the inner wall can significantly reduce the internal thermal resistance of heat pipe and then greatly enhance its heat transfer performance