Existence Is Chaos: Enhancing 3D Human Motion Prediction with Uncertainty Consideration

Human motion prediction is consisting in forecasting future body poses from historically observed sequences. It is a longstanding challenge due to motion's complex dynamics and uncertainty. Existing methods focus on building up complicated neural networks to model the motion dynamics. The predicted results are required to be strictly similar to the training samples with L2 loss in current training pipeline. However, little attention has been paid to the uncertainty property which is crucial to the prediction task. We argue that the recorded motion in training data could be an observation of possible future, rather than a predetermined result. In addition, existing works calculate the predicted error on each future frame equally during training, while recent work indicated that different frames could play different roles. In this work, a novel computationally efficient encoder-decoder model with uncertainty consideration is proposed, which could learn proper characteristics for future frames by a dynamic function. Experimental results on benchmark datasets demonstrate that our uncertainty consideration approach has obvious advantages both in quantity and quality. Moreover, the proposed method could produce motion sequences with much better quality that avoids the intractable shaking artefacts. We believe our work could provide a novel perspective to consider the uncertainty quality for the general motion prediction task and encourage the studies in this field. The code will be available in https://github.com/Motionpre/Adaptive-Salient-Loss-SAGGB.

GelSplitter: Tactile Reconstruction from Near Infrared and Visible Images

The GelSight-like visual tactile (VT) sensor has gained popularity as a high-resolution tactile sensing technology for robots, capable of measuring touch geometry using a single RGB camera. However, the development of multi-modal perception for VT sensors remains a challenge, limited by the mono camera. In this paper, we propose the GelSplitter, a new framework approach the multi-modal VT sensor with synchronized multi-modal cameras and resemble a more human-like tactile receptor. Furthermore, we focus on 3D tactile reconstruction and implement a compact sensor structure that maintains a comparable size to state-of-the-art VT sensors, even with the addition of a prism and a near infrared (NIR) camera. We also design a photometric fusion stereo neural network (PFSNN), which estimates surface normals of objects and reconstructs touch geometry from both infrared and visible images. Our results demonstrate that the accuracy of RGB and NIR fusion is higher than that of RGB images alone. Additionally, our GelSplitter framework allows for a flexible configuration of different camera sensor combinations, such as RGB and thermal imaging.

An Empirical Study on Fertility Proposals Using Multi-Grained Topic Analysis Methods

Fertility issues are closely related to population security, in 60 years China's population for the first time in a negative growth trend, the change of fertility policy is of great concern to the community. 2023 "two sessions" proposal "suggests that the country in the form of legislation, the birth of the registration of the cancellation of the marriage restriction" This topic was once a hot topic on the Internet, and "unbundling" the relationship between birth registration and marriage has become the focus of social debate. In this paper, we adopt co-occurrence semantic analysis, topic analysis and sentiment analysis to conduct multi-granularity semantic analysis of microblog comments. It is found that the discussion on the proposal of "removing marriage restrictions from birth registration" involves the individual, society and the state at three dimensions, and is detailed into social issues such as personal behaviour, social ethics and law, and national policy, with people's sentiment inclined to be negative in most of the topics. Based on this, eight proposals were made to provide a reference for governmental decision making and to form a reference method for researching public opinion on political issues.

Revisiting Automated Prompting: Are We Actually Doing Better?

Current literature demonstrates that Large Language Models (LLMs) are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks in a few-shot learning setting. An attempt to automate human-led prompting followed, with some progress achieved. In particular, subsequent work demonstrates automation can outperform fine-tuning in certain K-shot learning scenarios. In this paper, we revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings. We find that automated prompting does not consistently outperform simple manual prompts. Our work suggests that, in addition to fine-tuning, manual prompts should be used as a baseline in this line of research.

Shifting Perspective to See Difference: A Novel Multi-View Method for Skeleton based Action Recognition

Skeleton-based human action recognition is a longstanding challenge due to its complex dynamics. Some fine-grain details of the dynamics play a vital role in classification. The existing work largely focuses on designing incremental neural networks with more complicated adjacent matrices to capture the details of joints relationships. However, they still have difficulties distinguishing actions that have broadly similar motion patterns but belong to different categories. Interestingly, we found that the subtle differences in motion patterns can be significantly amplified and become easy for audience to distinct through specified view directions, where this property haven't been fully explored before. Drastically different from previous work, we boost the performance by proposing a conceptually simple yet effective Multi-view strategy that recognizes actions from a collection of dynamic view features. Specifically, we design a novel Skeleton-Anchor Proposal (SAP) module which contains a Multi-head structure to learn a set of views. For feature learning of different views, we introduce a novel Angle Representation to transform the actions under different views and feed the transformations into the baseline model. Our module can work seamlessly with the existing action classification model. Incorporated with baseline models, our SAP module exhibits clear performance gains on many challenging benchmarks. Moreover, comprehensive experiments show that our model consistently beats down the state-of-the-art and remains effective and robust especially when dealing with corrupted data. Related code will be available on https://github.com/ideal-idea/SAP .