Weakly Supervised Person Re-ID: Differentiable Graphical Learning and a New Benchmark

Weakly Supervised Tracklet Association Learning With Video Labels for Person Re-Identification

Supervised person re-identification (re-id) methods require expensive manual labeling costs. Although unsupervised re-id methods can reduce the requirement of the labeled datasets, the performance of these methods is lower than the supervised alternatives. Recently, some weakly supervised learning-based person re-id methods have been proposed, which is a balance between supervised and unsupervised learning. Nevertheless, most of these models require another auxiliary fully supervised datasets or ignore the interference of noisy tracklets. To address this problem, in this work, we formulate a weakly supervised tracklet association learning (WS-TAL) model only leveraging the video labels. Specifically, we first propose an intra-bag tracklet discrimination learning (ITDL) term. It can capture the associations between person identities and images by assigning pseudo labels to each person image in a bag. And then, the discriminative feature for each person is learned by utilizing the obtained associations after filtering the noisy tracklets. Based on that, a cross-bag tracklet association learning (CTAL) term is presented to explore the potential tracklet associations between bags by mining reliable positive tracklet pairs and hard negative pairs. Finally, these two complementary terms are jointly optimized to train our re-id model. Extensive experiments on the weakly labeled datasets demonstrate that WS-TAL achieves 88.1% and 90.3% rank-1 accuracy on the MARS and DukeMTMC-VideoReID datasets respectively. The performance of our model surpasses the state-of-the-art weakly supervised models by a large margin, even outperforms some fully supervised re-id models.

Deep Graph Metric Learning for Weakly Supervised Person Re-Identification

In conventional person re-identification (re-id), the images used for model training in the training probe set and training gallery set are all assumed to be instance-level samples that are manually labeled from raw surveillance video (likely with the assistance of detection) in a frame-by-frame manner. This labeling across multiple non-overlapping camera views from raw video surveillance is expensive and time consuming. To overcome these issues, we consider a weakly supervised person re-id modeling that aims to find the raw video clips where a given target person appears. In our weakly supervised setting, during training, given a sample of a person captured in one camera view, our weakly supervised approach aims to train a re-id model without further instance-level labeling for this person in another camera view. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. The weakly supervised person re-id is challenging since it not only suffers from the difficulties occurring in conventional person re-id (e.g., visual ambiguity and appearance variations caused by occlusions, pose variations, background clutter, etc.), but more importantly, is also challenged by weakly supervised information because the instance-level labels and the ground-truth locations for person instances (i.e., the ground-truth bounding boxes of person instances) are absent. To solve the weakly supervised person re-id problem, we develop deep graph metric learning (DGML). On the one hand, DGML measures the consistency between intra-video spatial graphs of consecutive frames, where the spatial graph captures neighborhood relationship about the detected person instances in each frame. On the other hand, DGML distinguishes the inter-video spatial graphs captured from different camera views at different sites simultaneously. To further explicitly embed weak supervision into the DGML and solve the weakly supervised person re-id problem, we introduce weakly supervised regularization (WSR), which utilizes multiple weak video-level labels to learn discriminative features by means of a weak identity loss and a cross-video alignment loss. We conduct extensive experiments to demonstrate the feasibility of the weakly supervised person re-id approach and its special cases (e.g., its bag-to-bag extension) and show that the proposed DGML is effective.

Learning From Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation

This paper addresses semi-supervised semantic segmentation by exploiting a small set of images with pixel-level annotations (strong supervisions) and a large set of images with only image-level annotations (weak supervisions). Most existing approaches aim to generate accurate pixel-level labels from weak supervisions. However, we observe that those generated labels still inevitably contain noisy labels. Motivated by this observation, we present a novel perspective and formulate this task as a problem of learning with pixel-level label noise. Existing noisy label methods, nevertheless, mainly aim at image-level tasks, which can not capture the relationship between neighboring labels in one image. Therefore, we propose a graph-based label noise detection and correction framework to deal with pixel-level noisy labels. In particular, for the generated pixel-level noisy labels from weak supervisions by Class Activation Map (CAM), we train a clean segmentation model with strong supervisions to detect the clean labels from these noisy labels according to the cross-entropy loss. Then, we adopt a superpixel-based graph to represent the relations of spatial adjacency and semantic similarity between pixels in one image. Finally we correct the noisy labels using a Graph Attention Network (GAT) supervised by detected clean labels. We comprehensively conduct experiments on PASCAL VOC 2012, PASCAL-Context, MS-COCO and Cityscapes datasets. The experimental results show that our proposed semi-supervised method achieves the state-of-the-art performances and even outperforms the fully-supervised models on PASCAL VOC 2012 and MS-COCO datasets in some cases.