He Zhao

Jianyang Xie, Yitian Zhao, Yanda Meng et al.

Spatial-temporal graph convolutional networks (ST-GCNs) showcase impressive performance in skeleton-based human action recognition (HAR). However, despite the development of numerous models, their recognition performance does not differ significantly after aligning the input settings. With this observation, we hypothesize that ST-GCNs are over-parameterized for HAR, a conjecture subsequently confirmed through experiments employing the lottery ticket hypothesis. Additionally, a novel sparse ST-GCNs generator is proposed, which trains a sparse architecture from a randomly initialized dense network while maintaining comparable performance levels to the dense components. Moreover, we generate multi-level sparsity ST-GCNs by integrating sparse structures at various sparsity levels and demonstrate that the assembled model yields a significant enhancement in HAR performance. Thorough experiments on four datasets, including NTU-RGB+D 60(120), Kinetics-400, and FineGYM, demonstrate that the proposed sparse ST-GCNs can achieve comparable performance to their dense components. Even with 95% fewer parameters, the sparse ST-GCNs exhibit a degradation of <1% in top-1 accuracy. Meanwhile, the multi-level sparsity ST-GCNs, which require only 66% of the parameters of the dense ST-GCNs, demonstrate an improvement of >1% in top-1 accuracy. The code is available at https://github.com/davelailai/Sparse-ST-GCN.

Thomas A. Langlois, H. Charles Zhao, Erin Grant et al.

Developments in machine learning interpretability techniques over the past decade have provided new tools to observe the image regions that are most informative for classification and localization in artificial neural networks (ANNs). Are the same regions similarly informative to human observers? Using data from 79 new experiments and 7,810 participants, we show that passive attention techniques reveal a significant overlap with human visual selectivity estimates derived from 6 distinct behavioral tasks including visual discrimination, spatial localization, recognizability, free-viewing, cued-object search, and saliency search fixations. We find that input visualizations derived from relatively simple ANN architectures probed using guided backpropagation methods are the best predictors of a shared component in the joint variability of the human measures. We validate these correlational results with causal manipulations using recognition experiments. We show that images masked with ANN attention maps were easier for humans to classify than control masks in a speeded recognition experiment. Similarly, we find that recognition performance in the same ANN models was likewise influenced by masking input images using human visual selectivity maps. This work contributes a new approach to evaluating the biological and psychological validity of leading ANNs as models of human vision: by examining their similarities and differences in terms of their visual selectivity to the information contained in images.

He Zhao, Richard P. Wildes

Video predictive understanding encompasses a wide range of efforts that are concerned with the anticipation of the unobserved future from the current as well as historical video observations. Action prediction is a major sub-area of video predictive understanding and is the focus of this review. This sub-area has two major subdivisions: early action recognition and future action prediction. Early action recognition is concerned with recognizing an ongoing action as soon as possible. Future action prediction is concerned with the anticipation of actions that follow those previously observed. In either case, the \textbf{\textit{causal}} relationship between the past, current, and potential future information is the main focus. Various mathematical tools such as Markov Chains, Gaussian Processes, Auto-Regressive modeling, and Bayesian recursive filtering are widely adopted jointly with computer vision techniques for these two tasks. However, these approaches face challenges such as the curse of dimensionality, poor generalization, and constraints from domain-specific knowledge. Recently, structures that rely on deep convolutional neural networks and recurrent neural networks have been extensively proposed for improving the performance of existing vision tasks, in general, and action prediction tasks, in particular. However, they have their own shortcomings, \eg reliance on massive training data and lack of strong theoretical underpinnings. In this survey, we start by introducing the major sub-areas of the broad area of video predictive understanding, which recently have received intensive attention and proven to have practical value. Next, a thorough review of various early action recognition and future action prediction algorithms are provided with suitably organized divisions. Finally, we conclude our discussion with future research directions.

He Zhao, Richard P. Wildes

Early action recognition (action prediction) from limited preliminary observations plays a critical role for streaming vision systems that demand real-time inference, as video actions often possess elongated temporal spans which cause undesired latency. In this study, we address action prediction by investigating how action patterns evolve over time in a spatial feature space. There are three key components to our system. First, we work with intermediate-layer ConvNet features, which allow for abstraction from raw data, while retaining spatial layout. Second, instead of propagating features per se, we propagate their residuals across time, which allows for a compact representation that reduces redundancy. Third, we employ a Kalman filter to combat error build-up and unify across prediction start times. Extensive experimental results on multiple benchmarks show that our approach leads to competitive performance in action prediction. Notably, we investigate the learned components of our system to shed light on their otherwise opaque natures in two ways. First, we document that our learned feature propagation module works as a spatial shifting mechanism under convolution to propagate current observations into the future. Thus, it captures flow-based image motion information. Second, the learned Kalman filter adaptively updates prior estimation to aid the sequence learning process.

Lingxiao He, Yinggang Wang, Wu Liu et al.

Re-identifying a person across multiple disjoint camera views is important for intelligent video surveillance, smart retailing and many other applications. However, existing person re-identification (ReID) methods are challenged by the ubiquitous occlusion over persons and suffer from performance degradation. This paper proposes a novel occlusion-robust and alignment-free model for occluded person ReID and extends its application to realistic and crowded scenarios. The proposed model first leverages the full convolution network (FCN) and pyramid pooling to extract spatial pyramid features. Then an alignment-free matching approach, namely Foreground-aware Pyramid Reconstruction (FPR), is developed to accurately compute matching scores between occluded persons, despite their different scales and sizes. FPR uses the error from robust reconstruction over spatial pyramid features to measure similarities between two persons. More importantly, we design an occlusion-sensitive foreground probability generator that focuses more on clean human body parts to refine the similarity computation with less contamination from occlusion. The FPR is easily embedded into any end-to-end person ReID models. The effectiveness of the proposed method is clearly demonstrated by the experimental results (Rank-1 accuracy) on three occluded person datasets: Partial REID (78.30\%), Partial iLIDS (68.08\%) and Occluded REID (81.00\%); and three benchmark person datasets: Market1501 (95.42\%), DukeMTMC (88.64\%) and CUHK03 (76.08\%)