Boundary and Relation Distillation for Semantic Segmentation

Recently, it has been revealed that small semantic segmentation (SS) models exhibit a tendency to make errors in maintaining boundary region completeness and preserving target region connectivity, despite their effective segmentation of the main object regions. To address these errors, we propose a targeted boundary and relation distillation (BRD) strategy using knowledge distillation from large teacher models to small student models. Specifically, the boundary distillation extracts explicit object boundaries from the hierarchical feature maps of the backbone network, subsequently enhancing the student model's mask quality in boundary regions. Concurrently, the relation distillation transfers implicit relations from the teacher model to the student model using pixel-level self-relation as a bridge, ensuring that the student's mask has strong target region connectivity. The proposed BRD is designed concretely for SS and is characterized by simplicity and efficiency. Through experimental evaluations on multiple SS datasets, including Pascal VOC 2012, Cityscapes, ADE20K, and COCO-Stuff 10K, we demonstrated that BRD significantly surpasses the current methods without increasing the inference costs, generating crisp region boundaries and smooth connecting regions that are challenging for small models.

On Non-Random Missing Labels in Semi-Supervised Learning

Semi-Supervised Learning (SSL) is fundamentally a missing label problem, in which the label Missing Not At Random (MNAR) problem is more realistic and challenging, compared to the widely-adopted yet naive Missing Completely At Random assumption where both labeled and unlabeled data share the same class distribution. Different from existing SSL solutions that overlook the role of "class" in causing the non-randomness, e.g., users are more likely to label popular classes, we explicitly incorporate "class" into SSL. Our method is three-fold: 1) We propose Class-Aware Propensity (CAP) that exploits the unlabeled data to train an improved classifier using the biased labeled data. 2) To encourage rare class training, whose model is low-recall but high-precision that discards too many pseudo-labeled data, we propose Class-Aware Imputation (CAI) that dynamically decreases (or increases) the pseudo-label assignment threshold for rare (or frequent) classes. 3) Overall, we integrate CAP and CAI into a Class-Aware Doubly Robust (CADR) estimator for training an unbiased SSL model. Under various MNAR settings and ablations, our method not only significantly outperforms existing baselines but also surpasses other label bias removal SSL methods. Please check our code at: https://github.com/JoyHuYY1412/CADR-FixMatch.

Third Party Risk Modelling and Assessment for Safe UAV Path Planning in Metropolitan Environments

Various applications of advanced air mobility (AAM) in urban environments facilitate our daily life and public services. As one of the key issues of realizing these applications autonomously, path planning problem has been studied with main objectives on minimizing travel distance, flight time and energy cost. However, AAM operations in metropolitan areas bring safety and society issues. Because most of AAM aircraft are unmanned aerial vehicles (UAVs) and they may fail to operate resulting in fatality risk, property damage risk and societal impacts (noise and privacy) to the public. To quantitatively assess these risks and mitigate them in planning phase, this paper proposes an integrated risk assessment model and develops a hybrid algorithm to solve the risk-based 3D path planning problem. The integrated risk assessment method considers probability and severity models of UAV impact ground people and vehicle. By introducing gravity model, the population density and traffic density are estimated in a finer scale, which enables more accurate risk assessment. The 3D risk-based path planning problem is first formulated as a special minimum cost flow problem. Then, a hybrid estimation of distribution algorithm (EDA) and risk-based A* (named as EDA-RA*) algorithm is proposed to solve the problem. To improve computational efficiency, k-means clustering method is incorporated into EDA-RA* to provide both global and local search heuristic information, which formed the EDA and fast risk-based A* algorithm we call EDA-FRA*. Case study results show that the risk assessment model can capture high risk areas and the generated risk map enables safe UAV path planning in urban complex environments.

Distilling Causal Effect of Data in Class-Incremental Learning

We propose a causal framework to explain the catastrophic forgetting in Class-Incremental Learning (CIL) and then derive a novel distillation method that is orthogonal to the existing anti-forgetting techniques, such as data replay and feature/label distillation. We first 1) place CIL into the framework, 2) answer why the forgetting happens: the causal effect of the old data is lost in new training, and then 3) explain how the existing techniques mitigate it: they bring the causal effect back. Based on the framework, we find that although the feature/label distillation is storage-efficient, its causal effect is not coherent with the end-to-end feature learning merit, which is however preserved by data replay. To this end, we propose to distill the Colliding Effect between the old and the new data, which is fundamentally equivalent to the causal effect of data replay, but without any cost of replay storage. Thanks to the causal effect analysis, we can further capture the Incremental Momentum Effect of the data stream, removing which can help to retain the old effect overwhelmed by the new data effect, and thus alleviate the forgetting of the old class in testing. Extensive experiments on three CIL benchmarks: CIFAR-100, ImageNet-Sub&Full, show that the proposed causal effect distillation can improve various state-of-the-art CIL methods by a large margin (0.72%--9.06%).

TransTrack: Multiple-Object Tracking with Transformer

Multiple-object tracking(MOT) is mostly dominated by complex and multi-step tracking-by-detection algorithm, which performs object detection, feature extraction and temporal association, separately. Query-key mechanism in single-object tracking(SOT), which tracks the object of the current frame by object feature of the previous frame, has great potential to set up a simple joint-detection-and-tracking MOT paradigm. Nonetheless, the query-key method is seldom studied due to its inability to detect new-coming objects. In this work, we propose TransTrack, a baseline for MOT with Transformer. It takes advantage of query-key mechanism and introduces a set of learned object queries into the pipeline to enable detecting new-coming objects. TransTrack has three main advantages: (1) It is an online joint-detection-and-tracking pipeline based on query-key mechanism. Complex and multi-step components in the previous methods are simplified. (2) It is a brand new architecture based on Transformer. The learned object query detects objects in the current frame. The object feature query from the previous frame associates those current objects with the previous ones. (3) For the first time, we demonstrate a much simple and effective method based on query-key mechanism and Transformer architecture could achieve competitive 65.8\% MOTA on the MOT17 challenge dataset. We hope TransTrack can provide a new perspective for multiple-object tracking. The code is available at: \url{https://github.com/PeizeSun/TransTrack}.

Learning to Segment the Tail

Real-world visual recognition requires handling the extreme sample imbalance in large-scale long-tailed data. We propose a "divide&conquer" strategy for the challenging LVIS task: divide the whole data into balanced parts and then apply incremental learning to conquer each one. This derives a novel learning paradigm: class-incremental few-shot learning, which is especially effective for the challenge evolving over time: 1) the class imbalance among the old-class knowledge review and 2) the few-shot data in new-class learning. We call our approach Learning to Segment the Tail (LST). In particular, we design an instance-level balanced replay scheme, which is a memory-efficient approximation to balance the instance-level samples from the old-class images. We also propose to use a meta-module for new-class learning, where the module parameters are shared across incremental phases, gaining the learning-to-learn knowledge incrementally, from the data-rich head to the data-poor tail. We empirically show that: at the expense of a little sacrifice of head-class forgetting, we can gain a significant 8.3% AP improvement for the tail classes with less than 10 instances, achieving an overall 2.0% AP boost for the whole 1,230 classes.