Bayesian Detector Combination for Object Detection with Crowdsourced Annotations

Acquiring fine-grained object detection annotations in unconstrained images is time-consuming, expensive, and prone to noise, especially in crowdsourcing scenarios. Most prior object detection methods assume accurate annotations; A few recent works have studied object detection with noisy crowdsourced annotations, with evaluation on distinct synthetic crowdsourced datasets of varying setups under artificial assumptions. To address these algorithmic limitations and evaluation inconsistency, we first propose a novel Bayesian Detector Combination (BDC) framework to more effectively train object detectors with noisy crowdsourced annotations, with the unique ability of automatically inferring the annotators' label qualities. Unlike previous approaches, BDC is model-agnostic, requires no prior knowledge of the annotators' skill level, and seamlessly integrates with existing object detection models. Due to the scarcity of real-world crowdsourced datasets, we introduce large synthetic datasets by simulating varying crowdsourcing scenarios. This allows consistent evaluation of different models at scale. Extensive experiments on both real and synthetic crowdsourced datasets show that BDC outperforms existing state-of-the-art methods, demonstrating its superiority in leveraging crowdsourced data for object detection. Our code and data are available at https://github.com/zhiqin1998/bdc.

ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation

The costly and time-consuming annotation process to produce large training sets for modelling semantic LiDAR segmentation methods has motivated the development of semi-supervised learning (SSL) methods. However, such SSL approaches often concentrate on employing consistency learning only for individual LiDAR representations. This narrow focus results in limited perturbations that generally fail to enable effective consistency learning. Additionally, these SSL approaches employ contrastive learning based on the sampling from a limited set of positive and negative embedding samples. This paper introduces a novel semi-supervised LiDAR semantic segmentation framework called ItTakesTwo (IT2). IT2 is designed to ensure consistent predictions from peer LiDAR representations, thereby improving the perturbation effectiveness in consistency learning. Furthermore, our contrastive learning employs informative samples drawn from a distribution of positive and negative embeddings learned from the entire training set. Results on public benchmarks show that our approach achieves remarkable improvements over the previous state-of-the-art (SOTA) methods in the field. The code is available at: https://github.com/yyliu01/IT2.

Learning to Complement and to Defer to Multiple Users

With the development of Human-AI Collaboration in Classification (HAI-CC), integrating users and AI predictions becomes challenging due to the complex decision-making process. This process has three options: 1) AI autonomously classifies, 2) learning to complement, where AI collaborates with users, and 3) learning to defer, where AI defers to users. Despite their interconnected nature, these options have been studied in isolation rather than as components of a unified system. In this paper, we address this weakness with the novel HAI-CC methodology, called Learning to Complement and to Defer to Multiple Users (LECODU). LECODU not only combines learning to complement and learning to defer strategies, but it also incorporates an estimation of the optimal number of users to engage in the decision process. The training of LECODU maximises classification accuracy and minimises collaboration costs associated with user involvement. Comprehensive evaluations across real-world and synthesized datasets demonstrate LECODU's superior performance compared to state-of-the-art HAI-CC methods. Remarkably, even when relying on unreliable users with high rates of label noise, LECODU exhibits significant improvement over both human decision-makers alone and AI alone.

CPM: Class-conditional Prompting Machine for Audio-visual Segmentation

Audio-visual segmentation (AVS) is an emerging task that aims to accurately segment sounding objects based on audio-visual cues. The success of AVS learning systems depends on the effectiveness of cross-modal interaction. Such a requirement can be naturally fulfilled by leveraging transformer-based segmentation architecture due to its inherent ability to capture long-range dependencies and flexibility in handling different modalities. However, the inherent training issues of transformer-based methods, such as the low efficacy of cross-attention and unstable bipartite matching, can be amplified in AVS, particularly when the learned audio query does not provide a clear semantic clue. In this paper, we address these two issues with the new Class-conditional Prompting Machine (CPM). CPM improves the bipartite matching with a learning strategy combining class-agnostic queries with class-conditional queries. The efficacy of cross-modal attention is upgraded with new learning objectives for the audio, visual and joint modalities. We conduct experiments on AVS benchmarks, demonstrating that our method achieves state-of-the-art (SOTA) segmentation accuracy.

Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning

Bayesian Neural Networks (BNNs) offer probability distributions for model parameters, enabling uncertainty quantification in predictions. However, they often underperform compared to deterministic neural networks. Utilizing mutual learning can effectively enhance the performance of peer BNNs. In this paper, we propose a novel approach to improve BNNs performance through deep mutual learning. The proposed approaches aim to increase diversity in both network parameter distributions and feature distributions, promoting peer networks to acquire distinct features that capture different characteristics of the input, which enhances the effectiveness of mutual learning. Experimental results demonstrate significant improvements in the classification accuracy, negative log-likelihood, and expected calibration error when compared to traditional mutual learning for BNNs.

Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth Estimation

In monocular depth estimation, unsupervised domain adaptation has recently been explored to relax the dependence on large annotated image-based depth datasets. However, this comes at the cost of training multiple models or requiring complex training protocols. We formulate unsupervised domain adaptation for monocular depth estimation as a consistency-based semi-supervised learning problem by assuming access only to the source domain ground truth labels. To this end, we introduce a pairwise loss function that regularises predictions on the source domain while enforcing perturbation consistency across multiple augmented views of the unlabelled target samples. Importantly, our approach is simple and effective, requiring only training of a single model in contrast to the prior work. In our experiments, we rely on the standard depth estimation benchmarks KITTI and NYUv2 to demonstrate state-of-the-art results compared to related approaches. Furthermore, we analyse the simplicity and effectiveness of our approach in a series of ablation studies. The code is available at \url{https://github.com/AmirMaEl/SemiSupMDE}.

Enhancing Multi-modal Learning: Meta-learned Cross-modal Knowledge Distillation for Handling Missing Modalities

In multi-modal learning, some modalities are more influential than others, and their absence can have a significant impact on classification/segmentation accuracy. Hence, an important research question is if it is possible for trained multi-modal models to have high accuracy even when influential modalities are absent from the input data. In this paper, we propose a novel approach called Meta-learned Cross-modal Knowledge Distillation (MCKD) to address this research question. MCKD adaptively estimates the importance weight of each modality through a meta-learning process. These dynamically learned modality importance weights are used in a pairwise cross-modal knowledge distillation process to transfer the knowledge from the modalities with higher importance weight to the modalities with lower importance weight. This cross-modal knowledge distillation produces a highly accurate model even with the absence of influential modalities. Differently from previous methods in the field, our approach is designed to work in multiple tasks (e.g., segmentation and classification) with minimal adaptation. Experimental results on the Brain tumor Segmentation Dataset 2018 (BraTS2018) and the Audiovision-MNIST classification dataset demonstrate the superiority of MCKD over current state-of-the-art models. Particularly in BraTS2018, we achieve substantial improvements of 3.51\% for enhancing tumor, 2.19\% for tumor core, and 1.14\% for the whole tumor in terms of average segmentation Dice score.

Frequency Attention for Knowledge Distillation

Knowledge distillation is an attractive approach for learning compact deep neural networks, which learns a lightweight student model by distilling knowledge from a complex teacher model. Attention-based knowledge distillation is a specific form of intermediate feature-based knowledge distillation that uses attention mechanisms to encourage the student to better mimic the teacher. However, most of the previous attention-based distillation approaches perform attention in the spatial domain, which primarily affects local regions in the input image. This may not be sufficient when we need to capture the broader context or global information necessary for effective knowledge transfer. In frequency domain, since each frequency is determined from all pixels of the image in spatial domain, it can contain global information about the image. Inspired by the benefits of the frequency domain, we propose a novel module that functions as an attention mechanism in the frequency domain. The module consists of a learnable global filter that can adjust the frequencies of student's features under the guidance of the teacher's features, which encourages the student's features to have patterns similar to the teacher's features. We then propose an enhanced knowledge review-based distillation model by leveraging the proposed frequency attention module. The extensive experiments with various teacher and student architectures on image classification and object detection benchmark datasets show that the proposed approach outperforms other knowledge distillation methods.

Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable Image Classification

Prototypical-part interpretable methods, e.g., ProtoPNet, enhance interpretability by connecting classification predictions to class-specific training prototypes, thereby offering an intuitive insight into their decision-making. Current methods rely on a discriminative classifier trained with point-based learning techniques that provide specific values for prototypes. Such prototypes have relatively low representation power due to their sparsity and potential redundancy, with each prototype containing no variability measure. In this paper, we present a new generative learning of prototype distributions, named Mixture of Gaussian-distributed Prototypes (MGProto), which are represented by Gaussian mixture models (GMM). Such an approach enables the learning of more powerful prototype representations since each learned prototype will own a measure of variability, which naturally reduces the sparsity given the spread of the distribution around each prototype, and we also integrate a prototype diversity objective function into the GMM optimisation to reduce redundancy. Incidentally, the generative nature of MGProto offers a new and effective way for detecting out-of-distribution samples. To improve the compactness of MGProto, we further propose to prune Gaussian-distributed prototypes with a low prior. Experiments on CUB-200-2011, Stanford Cars, Stanford Dogs, and Oxford-IIIT Pets datasets show that MGProto achieves state-of-the-art classification and OoD detection performances with encouraging interpretability results.

Learning to Complement with Multiple Humans (LECOMH): Integrating Multi-rater and Noisy-Label Learning into Human-AI Collaboration

The advent of learning with noisy labels (LNL), multi-rater learning, and human-AI collaboration has revolutionised the development of robust classifiers, enabling them to address the challenges posed by different types of data imperfections and complex decision processes commonly encountered in real-world applications. While each of these methodologies has individually made significant strides in addressing their unique challenges, the development of techniques that can simultaneously tackle these three problems remains underexplored. This paper addresses this research gap by integrating noisy-label learning, multi-rater learning, and human-AI collaboration with new benchmarks and the innovative Learning to Complement with Multiple Humans (LECOMH) approach. LECOMH optimises the level of human collaboration during testing, aiming to optimise classification accuracy while minimising collaboration costs that vary from 0 to M, where M is the maximum number of human collaborators. We quantitatively compare LECOMH with leading human-AI collaboration methods using our proposed benchmarks. LECOMH consistently outperforms the competition, with accuracy improving as collaboration costs increase. Notably, LECOMH is the only method enhancing human labeller performance across all benchmarks.