Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection

In this paper, we present a weakly-supervised RGB-D salient object detection model via scribble supervision. Specifically, as a multimodal learning task, we focus on effective multimodal representation learning via inter-modal mutual information regularization. In particular, following the principle of disentangled representation learning, we introduce a mutual information upper bound with a mutual information minimization regularizer to encourage the disentangled representation of each modality for salient object detection. Based on our multimodal representation learning framework, we introduce an asymmetric feature extractor for our multimodal data, which is proven more effective than the conventional symmetric backbone setting. We also introduce multimodal variational auto-encoder as stochastic prediction refinement techniques, which takes pseudo labels from the first training stage as supervision and generates refined prediction. Experimental results on benchmark RGB-D salient object detection datasets verify both effectiveness of our explicit multimodal disentangled representation learning method and the stochastic prediction refinement strategy, achieving comparable performance with the state-of-the-art fully supervised models. Our code and data are available at: https://github.com/baneitixiaomai/MIRV.Comment: IEEE Transactions on Circuits and Systems for Video Technology 202

Learning to see across domains and modalities

Deep learning has recently raised hopes and expectations as a general solution for many applications (computer vision, natural language processing, speech recognition, etc.); indeed it has proven effective, but it also showed a strong dependence on large quantities of data. Generally speaking, deep learning models are especially susceptible to overfitting, due to their large number of internal parameters. Luckily, it has also been shown that, even when data is scarce, a successful model can be trained by reusing prior knowledge. Thus, developing techniques for \textit{transfer learning} (as this process is known), in its broadest definition, is a crucial element towards the deployment of effective and accurate intelligent systems into the real world. This thesis will focus on a family of transfer learning methods applied to the task of visual object recognition, specifically image classification. The visual recognition problem is central to computer vision research: many desired applications, from robotics to information retrieval, demand the ability to correctly identify categories, places, and objects. Transfer learning is a general term, and specific settings have been given specific names: when the learner has access to only unlabeled data from the target domain (where the model should perform) and labeled data from a different domain (the source), the problem is called unsupervised domain adaptation (DA). The first part of this thesis will focus on three methods for this setting. The three presented techniques for domain adaptation are fully distinct: the first one proposes the use of Domain Alignment layers to structurally align the distributions of the source and target domains in feature space. While the general idea of aligning feature distribution is not novel, we distinguish our method by being one of the very few that do so without adding losses. The second method is based on GANs: we propose a bidirectional architecture that jointly learns how to map the source images into the target visual style and vice-versa, thus alleviating the domain shift at the pixel level. The third method features an adversarial learning process that transforms both the images and the features of both domains in order to map them to a common, agnostic, space. While the first part of the thesis presented general purpose DA methods, the second part will focus on the real life issues of robotic perception, specifically RGB-D recognition. Robotic platforms are usually not limited to color perception; very often they also carry a Depth camera. Unfortunately, the depth modality is rarely used for visual recognition due to the lack of pretrained models from which to transfer and little data to train one on from scratch. We will first explore the use of synthetic data as proxy for real images by training a Convolutional Neural Network (CNN) on virtual depth maps, rendered from 3D CAD models, and then testing it on real robotic datasets. The second approach leverages the existence of RGB pretrained models, by learning how to map the depth data into the most discriminative RGB representation and then using existing models for recognition. This second technique is actually a pretty generic Transfer Learning method which can be applied to share knowledge across modalities

Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic Segmentation

Using multiple spatial modalities has been proven helpful in improving semantic segmentation performance. However, there are several real-world challenges that have yet to be addressed: (a) improving label efficiency and (b) enhancing robustness in realistic scenarios where modalities are missing at the test time. To address these challenges, we first propose a simple yet efficient multi-modal fusion mechanism Linear Fusion, that performs better than the state-of-the-art multi-modal models even with limited supervision. Second, we propose M3L: Multi-modal Teacher for Masked Modality Learning, a semi-supervised framework that not only improves the multi-modal performance but also makes the model robust to the realistic missing modality scenario using unlabeled data. We create the first benchmark for semi-supervised multi-modal semantic segmentation and also report the robustness to missing modalities. Our proposal shows an absolute improvement of up to 10% on robust mIoU above the most competitive baselines. Our code is available at https://github.com/harshm121/M3

A Dimensional Structure based Knowledge Distillation Method for Cross-Modal Learning

Due to limitations in data quality, some essential visual tasks are difficult to perform independently. Introducing previously unavailable information to transfer informative dark knowledge has been a common way to solve such hard tasks. However, research on why transferred knowledge works has not been extensively explored. To address this issue, in this paper, we discover the correlation between feature discriminability and dimensional structure (DS) by analyzing and observing features extracted from simple and hard tasks. On this basis, we express DS using deep channel-wise correlation and intermediate spatial distribution, and propose a novel cross-modal knowledge distillation (CMKD) method for better supervised cross-modal learning (CML) performance. The proposed method enforces output features to be channel-wise independent and intermediate ones to be uniformly distributed, thereby learning semantically irrelevant features from the hard task to boost its accuracy. This is especially useful in specific applications where the performance gap between dual modalities is relatively large. Furthermore, we collect a real-world CML dataset to promote community development. The dataset contains more than 10,000 paired optical and radar images and is continuously being updated. Experimental results on real-world and benchmark datasets validate the effectiveness of the proposed method

On Deep Machine Learning for Multi-view Object Detection and Neural Scene Rendering

This thesis addresses two contemporary computer vision tasks using a set of multiple-view imagery, namely the joint use of multi-view images to improve object detection and neural scene rendering via a novel volumetric input encoding for Neural Radiance Fields (NeRF). While the former focuses on improving the accuracy of object detection, the latter contribution allows for better scene reconstruction, which ultimately can be exploited to generate novel views and perform multi-view object detection. Notwithstanding the significant advances in automatic object detection in the last decade, multi-view object detection has received little attention. For this reason, two contributions regarding multi-view object detection in the absence of explicit camera pose information are presented in this thesis. First, a multi-view epipolar filtering technique is introduced, using the distance of the detected object centre to a corresponding epipolar line as an additional probabilistic confidence. This technique removes false positives without a corresponding detection in other views, giving greater confidence to consistent detections across the views. The second contribution adds an attention-based layer, called Multi-view Vision Transformer, to the backbone of a deep machine learning object detector, effectively aggregating features from different views and creating a multi-view aware representation. The final contribution explores another application for multi-view imagery, namely novel volumetric input encoding of NeRF. The proposed method derives an analytical solution for the average value of a sinusoidal (inducing a high-frequency component) within a pyramidal frustum region, whereas previous state-of-the-art NeRF methods approximate this with a Gaussian distribution. This parameterisation obtains a better representation of regions where the Gaussian approximation is poor, allowing more accurate synthesis of distant areas and depth map estimation. Experimental evaluation is carried out across multiple established benchmark datasets to compare the proposed methods against contemporary state-of-the-art architectures such that the efficacy of the proposed methods can be both quantitively and qualitatively illustrated

New deep learning approaches to domain adaptation and their applications in 3D hand pose estimation

This study investigates several methods for using artificial intelligence to give machines the ability to see. It introduced several methods for image recognition that are more accurate and efficient compared to the existing approaches