Learning to re-identify or retrieve a group of people across non-overlapped camera systems has important applications in video surveillance. However, most existing methods focus on (single) person re-identification (re-id), ignoring the fact that people often walk in groups in real scenarios. In this work, we take a step further and consider employing context information for identifying groups of people, i.e., group re-id. On the one hand, group re-id is more challenging than single person re-id, since it requires both a robust modeling of local individual person appearance (with different illumination conditions, pose/viewpoint variations, and occlusions), as well as full awareness of global group structures (with group layout and group member variations). On the other hand, we believe that person re-id can be greatly enhanced by incorporating additional visual context from neighboring group members, a task which we formulate as group-aware (single) person re-id. In this paper, we propose a novel unified framework based on graph neural networks to simultaneously address the above two group-based re-id tasks, i.e., group re-id and group-aware person re-id. Specifically, we construct a context graph with group members as its nodes to exploit dependencies among different people. A multi-level attention mechanism is developed to formulate both intra-group and inter-group context, with an additional self-attention module for robust graph-level representations by attentively aggregating node-level features. The proposed model can be directly generalized to tackle group-aware person re-id using node-level representations. Meanwhile, to facilitate the deployment of deep learning models on these tasks, we build a new group re-id dataset which contains more than $3.8K$ images with $1.5K$ annotated groups, an order of magnitude larger than existing group re-id datasets. Extensive experiments on the novel dataset as well as three existing datasets clearly demonstrate the effectiveness of the proposed framework for both group-based re-id tasks.

In semi-supervised learning (SSL), many approaches follow the effective self-training paradigm with consistency regularization, utilizing threshold heuristics to alleviate label noise. However, such threshold heuristics lead to the underutilization of crucial discriminative information from the excluded data. In this paper, we present OTAMatch, a novel SSL framework that reformulates pseudo-labeling as an optimal transport (OT) assignment problem and simultaneously exploits data with high confidence to mitigate the confirmation bias. Firstly, OTAMatch models the pseudo-label allocation task as a convex minimization problem, facilitating end-to-end optimization with all pseudo-labels and employing the Sinkhorn-Knopp algorithm for efficient approximation. Meanwhile, we incorporate epsilon-greedy posterior regularization and curriculum bias correction strategies to constrain the distribution of OT assignments, improving the robustness with noisy pseudo-labels. Secondly, we propose PseudoNCE, which explicitly exploits pseudo-label consistency with threshold heuristics to maximize mutual information within self-training, significantly boosting the balance of convergence speed and performance. Consequently, our proposed approach achieves competitive performance on various SSL benchmarks. Specifically, OTAMatch substantially outperforms the previous state-of-the-art SSL algorithms in realistic and challenging scenarios, exemplified by a no table 9 .45% error rate reduction over SoftMatch on ImageNet with 100K-label split, underlining its robustness and effectiveness.

This paper investigates compressing a pre-trained deep object detector to a lightweight one by channel pruning, which has proved effective and flexible in promoting efficiency. However, the majority of existing works trim channels based on a monotonous criterion for general purposes, i.e., the importance to the task-specific loss. They are prone to overly prune intermediate layers and simultaneously leave large intra-layer redundancy, severely deteriorating the detection accuracy. To address the issues above, we propose a novel channel pruning approach with multi-granular importance estimation (MIEP), consisting of the Feature-level Object-sensitive Importance (FOI) and the Intra-layer Redundancy-aware Importance (IRI). The former puts large weights on channels that are critical for object representation through the guidance of object features from the pre-trained model, and mitigates over-pruning when combined with the task-specific loss. The latter groups highly correlated channels based on clustering, which are subsequently pruned with priority to decrease redundancy. Extensive experiments on the COCO and VOC benchmarks demonstrate that MIEP remarkably outperforms the state-of-the-art channel pruning approaches, achieves a better balance between accuracy and efficiency compared to lightweight object detectors, and generalizes well to various detection frameworks (e.g., Faster-RCNN and FSAF) and tasks (e.g., classification).

In this paper, we propose a novel Transformer based approach, namely Cross-modal Contrastive Masked AutoEncoder (C²MAE), to Self-Supervised Learning (SSL) on compressed videos. A unified Transformer encoder is employed to discover relationships of visual tokens from RGBs, motion vectors and residuals. A hybrid SSL framework is proposed, which combines the complementary advantages of Masked Image Modeling (MIM) and Contrastive Learning (CL) pretext tasks, for powerful representation learning. The MIM branch extends VideoMAE by a new Fine-Grained Motion-aware Masking (FGMM) strategy and a modified Multi-modal Reconstruction (MR) task, where FGMM computes motion saliency maps as motion priors to guide the masks so that it well fits for the data properties in the compressed domain and the MR task highlights the reconstruction of raw videos by joint representations from corresponding compressed videos in addition to that in each single modality. The CL branch introduces the Contrastive Cross-modal Learning (CCL) module, and the features from a compressed video clip and the ones from its raw video counterpart are compared instead of widely used augmented data. Due to these designs, C²MAE significantly enhances interactions across modalities to compensate the sparsity of I-frames and the coarse and noisy nature of P-frames, thus delivering much stronger pre-trained models. Extensive experiments are conducted on the UCF-101, HMDB-51 and Kinetics-400 benchmarks with state-of-the-art results reported, demonstrating its effectiveness.