View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums

Learning to understand and predict future motions or behaviors for agents like humans and robots are critical to various autonomous platforms, such as behavior analysis, robot navigation, and self-driving cars. Intrinsic factors such as agents' diversified personalities and decision-making styles bring rich and diverse changes and multi-modal characteristics to their future plannings. Besides, the extrinsic interactive factors have also brought rich and varied changes to their trajectories. Previous methods mostly treat trajectories as time sequences, and reach great prediction performance. In this work, we try to focus on agents' trajectories in another view, i.e., the Fourier spectrums, to explore their future behavior rules in a novel hierarchical way. We propose the Transformer-based V model, which concatenates two continuous keypoints estimation and spectrum interpolation sub-networks, to model and predict agents' trajectories with spectrums in the keypoints and interactions levels respectively. Experimental results show that V outperforms most of current state-of-the-art methods on ETH-UCY and SDD trajectories dataset for about 15\% quantitative improvements, and performs better qualitative results.

HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning

Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge transfer, a common (latent) space is adopted for associating the visual and semantic domains in ZSL. However, existing common space learning methods align the semantic and visual domains by merely mitigating distribution disagreement through one-step adaptation. This strategy is usually ineffective due to the heterogeneous nature of the feature representations in the two domains, which intrinsically contain both distribution and structure variations. To address this and advance ZSL, we propose a novel hierarchical semantic-visual adaptation (HSVA) framework. Specifically, HSVA aligns the semantic and visual domains by adopting a hierarchical two-step adaptation, i.e., structure adaptation and distribution adaptation. In the structure adaptation step, we take two task-specific encoders to encode the source data (visual domain) and the target data (semantic domain) into a structure-aligned common space. To this end, a supervised adversarial discrepancy (SAD) module is proposed to adversarially minimize the discrepancy between the predictions of two task-specific classifiers, thus making the visual and semantic feature manifolds more closely aligned. In the distribution adaptation step, we directly minimize the Wasserstein distance between the latent multivariate Gaussian distributions to align the visual and semantic distributions using a common encoder. Finally, the structure and distribution adaptation are derived in a unified framework under two partially-aligned variational autoencoders. Extensive experiments on four benchmark datasets demonstrate that HSVA achieves superior performance on both conventional and generalized ZSL. The code is available at \url{https://github.com/shiming-chen/HSVA} .

FREE: Feature Refinement for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gap and seen-unseen bias. However, most existing methods directly use feature extraction models trained on ImageNet alone, ignoring the cross-dataset bias between ImageNet and GZSL benchmarks. Such a bias inevitably results in poor-quality visual features for GZSL tasks, which potentially limits the recognition performance on both seen and unseen classes. In this paper, we propose a simple yet effective GZSL method, termed feature refinement for generalized zero-shot learning (FREE), to tackle the above problem. FREE employs a feature refinement (FR) module that incorporates \textit{semantic$\rightarrow$visual} mapping into a unified generative model to refine the visual features of seen and unseen class samples. Furthermore, we propose a self-adaptive margin center loss (SAMC-loss) that cooperates with a semantic cycle-consistency loss to guide FR to learn class- and semantically-relevant representations, and concatenate the features in FR to extract the fully refined features. Extensive experiments on five benchmark datasets demonstrate the significant performance gain of FREE over its baseline and current state-of-the-art methods. Our codes are available at https://github.com/shiming-chen/FREE .

MSN: Multi-Style Network for Trajectory Prediction

It is essential but challenging to predict future trajectories of various agents in complex scenes. Whether it is internal personality factors of agents, interactive behavior of the neighborhood, or the influence of surroundings, it will have an impact on their future behavior styles. It means that even for the same physical type of agents, there are huge differences in their behavior preferences. Although recent works have made significant progress in studying agents' multi-modal plannings, most of them still apply the same prediction strategy to all agents, which makes them difficult to fully show the multiple styles of vast agents. In this paper, we propose the Multi-Style Network (MSN) to focus on this problem by divide agents' preference styles into several hidden behavior categories adaptively and train each category's prediction network separately, therefore giving agents all styles of predictions simultaneously. Experiments demonstrate that our deterministic MSN-D and generative MSN-G outperform many recent state-of-the-art methods and show better multi-style characteristics in the visualized results.

BGM: Building a Dynamic Guidance Map without Visual Images for Trajectory Prediction

Visual images usually contain the informative context of the environment, thereby helping to predict agents' behaviors. However, they hardly impose the dynamic effects on agents' actual behaviors due to the respectively fixed semantics. To solve this problem, we propose a deterministic model named BGM to construct a guidance map to represent the dynamic semantics, which circumvents to use visual images for each agent to reflect the difference of activities in different periods. We first record all agents' activities in the scene within a period close to the current to construct a guidance map and then feed it to a Context CNN to obtain their context features. We adopt a Historical Trajectory Encoder to extract the trajectory features and then combine them with the context feature as the input of the social energy based trajectory decoder, thus obtaining the prediction that meets the social rules. Experiments demonstrate that BGM achieves state-of-the-art prediction accuracy on the two widely used ETH and UCY datasets and handles more complex scenarios.

Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise.

A Spatial-Temporal Attentive Network with Spatial Continuity for Trajectory Prediction

It remains challenging to automatically predict the multi-agent trajectory due to multiple interactions including agent to agent interaction and scene to agent interaction. Although recent methods have achieved promising performance, most of them just consider spatial influence of the interactions and ignore the fact that temporal influence always accompanies spatial influence. Moreover, those methods based on scene information always require extra segmented scene images to generate multiple socially acceptable trajectories. To solve these limitations, we propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC). First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity. Experiments are performed on the two widely used ETH-UCY datasets and demonstrate that the proposed model achieves state-of-the-art prediction accuracy and handles more complex scenarios.

Similarity-DT: Kernel Similarity Embedding for Dynamic Texture Synthesis

Dynamic texture (DT) exhibits statistical stationarity in the spatial domain and stochastic repetitiveness in the temporal dimension, indicating that different frames of DT possess high similarity correlation. However, there are no DT synthesis methods to consider the similarity prior for representing DT instead, which can explicitly capture the homogeneous and heterogeneous correlation between different frames of DT. In this paper, we propose a novel DT synthesis method (named Similarity-DT), which embeds the similarity prior into the representation of DT. Specifically, we first raise two hypotheses: the content of texture video frames varies over time-to-time, while the more closed frames should be more similar; the transition between frame-to-frame could be modeled as a linear or nonlinear function to capture the similarity correlation. Then, our proposed Similarity-DT integrates kernel learning and extreme learning machine (ELM) into a powerful unified synthesis model to learn kernel similarity embedding to represent the spatial-temporal transition among frame-to-frame of DTs. Extensive experiments on DT videos collected from internet and two benchmark datasets, i.e., Gatech Graphcut Textures and Dyntex, demonstrate that the learned kernel similarity embedding effectively exhibits the discriminative representation for DTs. Hence our method is capable of preserving long-term temporal continuity of the synthesized DT sequences with excellent sustainability and generalization. We also show that our method effectively generates realistic DT videos with fast speed and low computation, compared with the state-of-the-art approaches.