Distilling Invariant Representations with Dual Augmentation

Knowledge distillation (KD) has been widely used to transfer knowledge from large, accurate models (teachers) to smaller, efficient ones (students). Recent methods have explored enforcing consistency by incorporating causal interpretations to distill invariant representations. In this work, we extend this line of research by introducing a dual augmentation strategy to promote invariant feature learning in both teacher and student models. Our approach leverages different augmentations applied to both models during distillation, pushing the student to capture robust, transferable features. This dual augmentation strategy complements invariant causal distillation by ensuring that the learned representations remain stable across a wider range of data variations and transformations. Extensive experiments on CIFAR-100 demonstrate the effectiveness of this approach, achieving competitive results in same-architecture KD.

SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual Representations

Contrastive learning has become a dominant approach in self-supervised visual representation learning, with hard negatives-samples that closely resemble the anchor-being key to enhancing the discriminative power of learned representations. However, efficiently leveraging hard negatives remains a challenge due to the difficulty in identifying and incorporating them without significantly increasing computational costs. To address this, we introduce SynCo (Synthetic Negatives in Contrastive learning), a novel contrastive learning approach that improves model performance by generating synthetic hard negatives. Built on the MoCo framework, SynCo introduces six novel strategies for creating diverse synthetic hard negatives that can be generated on-the-fly with minimal computational overhead. SynCo achieves faster training and better representation learning, achieving a top-1 accuracy of 68.1% in ImageNet linear evaluation after only 200 epochs on pretraining, surpassing MoCo's 67.5% with the same ResNet-50 encoder. Additionally, it transfers more effectively to detection tasks: on the PASCAL VOC, it outperforms both the supervised baseline and MoCo, achieving an AP of 82.5%; on the COCO dataset, it sets a new benchmark with 40.4% AP for bounding box detection and 35.4% AP for instance segmentation. Our synthetic hard negative generation procedure significantly enhances the quality of visual representations learned through self-supervised contrastive learning. Code is available at https://github.com/giakoumoglou/synco.

Passenger hazard perception based on EEG signals for highly automated driving vehicles

Enhancing the safety of autonomous vehicles is crucial, especially given recent accidents involving automated systems. As passengers in these vehicles, humans' sensory perception and decision-making can be integrated with autonomous systems to improve safety. This study explores neural mechanisms in passenger-vehicle interactions, leading to the development of a Passenger Cognitive Model (PCM) and the Passenger EEG Decoding Strategy (PEDS). Central to PEDS is a novel Convolutional Recurrent Neural Network (CRNN) that captures spatial and temporal EEG data patterns. The CRNN, combined with stacking algorithms, achieves an accuracy of $85.0\% \pm 3.18\%$. Our findings highlight the predictive power of pre-event EEG data, enhancing the detection of hazardous scenarios and offering a network-driven framework for safer autonomous vehicles.

Size Aware Cross-shape Scribble Supervision for Medical Image Segmentation

Scribble supervision, a common form of weakly supervised learning, involves annotating pixels using hand-drawn curve lines, which helps reduce the cost of manual labelling. This technique has been widely used in medical image segmentation tasks to fasten network training. However, scribble supervision has limitations in terms of annotation consistency across samples and the availability of comprehensive groundtruth information. Additionally, it often grapples with the challenge of accommodating varying scale targets, particularly in the context of medical images. In this paper, we propose three novel methods to overcome these challenges, namely, 1) the cross-shape scribble annotation method; 2) the pseudo mask method based on cross shapes; and 3) the size-aware multi-branch method. The parameter and structure design are investigated in depth. Experimental results show that the proposed methods have achieved significant improvement in mDice scores across multiple polyp datasets. Notably, the combination of these methods outperforms the performance of state-of-the-art scribble supervision methods designed for medical image segmentation.

Mean Height Aided Post-Processing for Pedestrian Detection

The design of pedestrian detectors seldom considers the unique characteristics of this task and usually follows the common strategies for general object detection. To explore the potential of these characteristics, we take the perspective effect in pedestrian datasets as an example and propose the mean height aided suppression for post-processing. This method rejects predictions that fall at levels with a low possibility of containing any pedestrians or that have an abnormal height compared to the average. To achieve this, the existence score and mean height generators are proposed. Comprehensive experiments on various datasets and detectors are performed; the choice of hyper-parameters is discussed in depth. The proposed method is easy to implement and is plug-and-play. Results show that the proposed methods significantly improve detection accuracy when applied to different existing pedestrian detectors and datasets. The combination of mean height aided suppression with particular detectors outperforms state-of-the-art pedestrian detectors on Caltech and Citypersons datasets.

Relational Representation Distillation

Knowledge distillation (KD) is an effective method for transferring knowledge from a large, well-trained teacher model to a smaller, more efficient student model. Despite its success, one of the main challenges in KD is ensuring the efficient transfer of complex knowledge while maintaining the student's computational efficiency. Unlike previous works that applied contrastive objectives promoting explicit negative instances, we introduce Relational Representation Distillation (RRD). Our approach leverages pairwise similarities to explore and reinforce the relationships between the teacher and student models. Inspired by self-supervised learning principles, it uses a relaxed contrastive loss that focuses on similarity rather than exact replication. This method aligns the output distributions of teacher samples in a large memory buffer, improving the robustness and performance of the student model without the need for strict negative instance differentiation. Our approach demonstrates superior performance on CIFAR-100, outperforming traditional KD techniques and surpassing 13 state-of-the-art methods. It also transfers successfully to other datasets like Tiny ImageNet and STL-10. The code will be made public soon.

Invariant Consistency for Knowledge Distillation

Knowledge distillation (KD) involves transferring the knowledge from one neural network to another, often from a larger, well-trained model (teacher) to a smaller, more efficient model (student). Traditional KD methods minimize the Kullback-Leibler (KL) divergence between the probabilistic outputs of the teacher and student networks. However, this approach often overlooks crucial structural knowledge embedded within the teacher's network. In this paper, we introduce Invariant Consistency Distillation (ICD), a novel methodology designed to enhance KD by ensuring that the student model's representations are consistent with those of the teacher. Our approach combines contrastive learning with an explicit invariance penalty, capturing significantly more information from the teacher's representation of the data. Our results on CIFAR-100 demonstrate that ICD outperforms traditional KD techniques and surpasses 13 state-of-the-art methods. In some cases, the student model even exceeds the teacher model in terms of accuracy. Furthermore, we successfully transfer our method to other datasets, including Tiny ImageNet and STL-10. The code will be made public soon.

G-FARS: Gradient-Field-based Auto-Regressive Sampling for 3D Part Grouping

This paper proposes a novel task named "3D part grouping". Suppose there is a mixed set containing scattered parts from various shapes. This task requires algorithms to find out every possible combination among all the parts. To address this challenge, we propose the so called Gradient Field-based Auto-Regressive Sampling framework (G-FARS) tailored specifically for the 3D part grouping task. In our framework, we design a gradient-field-based selection graph neural network (GNN) to learn the gradients of a log conditional probability density in terms of part selection, where the condition is the given mixed part set. This innovative approach, implemented through the gradient-field-based selection GNN, effectively captures complex relationships among all the parts in the input. Upon completion of the training process, our framework becomes capable of autonomously grouping 3D parts by iteratively selecting them from the mixed part set, leveraging the knowledge acquired by the trained gradient-field-based selection GNN. Our code is available at: https://github.com/J-F-Cheng/G-FARS-3DPartGrouping.

A review on discriminative self-supervised learning methods

In the field of computer vision, self-supervised learning has emerged as a method to extract robust features from unlabeled data, where models derive labels autonomously from the data itself, without the need for manual annotation. This paper provides a comprehensive review of discriminative approaches of self-supervised learning within the domain of computer vision, examining their evolution and current status. Through an exploration of various methods including contrastive, self-distillation, knowledge distillation, feature decorrelation, and clustering techniques, we investigate how these approaches leverage the abundance of unlabeled data. Finally, we have comparison of self-supervised learning methods on the standard ImageNet classification benchmark.

A Multimodal Approach for Cross-Domain Image Retrieval

Image generators are gaining vast amount of popularity and have rapidly changed how digital content is created. With the latest AI technology, millions of high quality images are being generated by the public, which are constantly motivating the research community to push the limits of generative models to create more complex and realistic images. This paper focuses on Cross-Domain Image Retrieval (CDIR) which can be used as an additional tool to inspect collections of generated images by determining the level of similarity between images in a dataset. An ideal retrieval system would be able to generalize to unseen complex images from multiple domains (e.g., photos, drawings and paintings). To address this goal, we propose a novel caption-matching approach that leverages multimodal language-vision architectures pre-trained on large datasets. The method is tested on DomainNet and Office-Home datasets and consistently achieves state-of-the-art performance over the latest approaches in the literature for cross-domain image retrieval. In order to verify the effectiveness with AI-generated images, the method was also put to test with a database composed by samples collected from Midjourney, which is a widely used generative platform for content creation.