DT-JRD: Deep Transformer based Just Recognizable Difference Prediction Model for Video Coding for Machines

Just Recognizable Difference (JRD) represents the minimum visual difference that is detectable by machine vision, which can be exploited to promote machine vision oriented visual signal processing. In this paper, we propose a Deep Transformer based JRD (DT-JRD) prediction model for Video Coding for Machines (VCM), where the accurately predicted JRD can be used reduce the coding bit rate while maintaining the accuracy of machine tasks. Firstly, we model the JRD prediction as a multi-class classification and propose a DT-JRD prediction model that integrates an improved embedding, a content and distortion feature extraction, a multi-class classification and a novel learning strategy. Secondly, inspired by the perception property that machine vision exhibits a similar response to distortions near JRD, we propose an asymptotic JRD loss by using Gaussian Distribution-based Soft Labels (GDSL), which significantly extends the number of training labels and relaxes classification boundaries. Finally, we propose a DT-JRD based VCM to reduce the coding bits while maintaining the accuracy of object detection. Extensive experimental results demonstrate that the mean absolute error of the predicted JRD by the DT-JRD is 5.574, outperforming the state-of-the-art JRD prediction model by 13.1%. Coding experiments shows that comparing with the VVC, the DT-JRD based VCM achieves an average of 29.58% bit rate reduction while maintaining the object detection accuracy.

FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction

Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. Despite its broad applications in fields such as computer vision, graph learning, and natural language processing, the development of a data projection model that can be effectively used to visualize data in the context of FL is crucial yet remains heavily under-explored. Neighbor embedding (NE) is an essential technique for visualizing complex high-dimensional data, but collaboratively learning a joint NE model is difficult. The key challenge lies in the objective function, as effective visualization algorithms like NE require computing loss functions among pairs of data. In this paper, we introduce \textsc{FedNE}, a novel approach that integrates the \textsc{FedAvg} framework with the contrastive NE technique, without any requirements of shareable data. To address the lack of inter-client repulsion which is crucial for the alignment in the global embedding space, we develop a surrogate loss function that each client learns and shares with each other. Additionally, we propose a data-mixing strategy to augment the local data, aiming to relax the problems of invisible neighbors and false neighbors constructed by the local $k$NN graphs. We conduct comprehensive experiments on both synthetic and real-world datasets. The results demonstrate that our \textsc{FedNE} can effectively preserve the neighborhood data structures and enhance the alignment in the global embedding space compared to several baseline methods.

Global-Local Progressive Integration Network for Blind Image Quality Assessment

Vision transformers (ViTs) excel in computer vision for modeling long-term dependencies, yet face two key challenges for image quality assessment (IQA): discarding fine details during patch embedding, and requiring extensive training data due to lack of inductive biases. In this study, we propose a Global-Local progressive INTegration network for IQA, called GlintIQA, to address these issues through three key components: 1) Hybrid feature extraction combines ViT-based global feature extractor (VGFE) and convolutional neural networks (CNNs)-based local feature extractor (CLFE) to capture global coarse-grained features and local fine-grained features, respectively. The incorporation of CNNs mitigates the patch-level information loss and inductive bias constraints inherent to ViT architectures. 2) Progressive feature integration leverages diverse kernel sizes in embedding to spatially align coarse- and fine-grained features, and progressively aggregate these features by interactively stacking channel-wise attention and spatial enhancement modules to build effective quality-aware representations. 3) Content similarity-based labeling approach is proposed that automatically assigns quality labels to images with diverse content based on subjective quality scores. This addresses the scarcity of labeled training data in synthetic datasets and bolsters model generalization. The experimental results demonstrate the efficacy of our approach, yielding 5.04% average SROCC gains on cross-authentic dataset evaluations. Moreover, our model and its counterpart pre-trained on the proposed dataset respectively exhibited 5.40% and 13.23% improvements on across-synthetic datasets evaluation. The codes and proposed dataset will be released at https://github.com/XiaoqiWang/GlintIQA.

Symmetric Multi-Similarity Loss for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2024

In this report, we present our champion solution for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge in CVPR 2024. Essentially, this challenge differs from traditional visual-text retrieval tasks by providing a correlation matrix that acts as a set of soft labels for video-text clip combinations. However, existing loss functions have not fully exploited this information. Motivated by this, we propose a novel loss function, Symmetric Multi-Similarity Loss, which offers a more precise learning objective. Together with tricks and ensemble learning, the model achieves 63.76% average mAP and 74.25% average nDCG on the public leaderboard, demonstrating the effectiveness of our approach. Our code will be released at: https://github.com/xqwang14/SMS-Loss/tree/main

USE: Universal Segment Embeddings for Open-Vocabulary Image Segmentation

The open-vocabulary image segmentation task involves partitioning images into semantically meaningful segments and classifying them with flexible text-defined categories. The recent vision-based foundation models such as the Segment Anything Model (SAM) have shown superior performance in generating class-agnostic image segments. The main challenge in open-vocabulary image segmentation now lies in accurately classifying these segments into text-defined categories. In this paper, we introduce the Universal Segment Embedding (USE) framework to address this challenge. This framework is comprised of two key components: 1) a data pipeline designed to efficiently curate a large amount of segment-text pairs at various granularities, and 2) a universal segment embedding model that enables precise segment classification into a vast range of text-defined categories. The USE model can not only help open-vocabulary image segmentation but also facilitate other downstream tasks (e.g., querying and ranking). Through comprehensive experimental studies on semantic segmentation and part segmentation benchmarks, we demonstrate that the USE framework outperforms state-of-the-art open-vocabulary segmentation methods.

Regression-free Blind Image Quality Assessment

Regression-based blind image quality assessment (IQA) models are susceptible to biased training samples, leading to a biased estimation of model parameters. To mitigate this issue, we propose a regression-free framework for image quality evaluation, which is founded upon retrieving similar instances by incorporating semantic and distortion features. The motivation behind this approach is rooted in the observation that the human visual system (HVS) has analogous visual responses to semantically similar image contents degraded by the same distortion. The proposed framework comprises two classification-based modules: semantic-based classification (SC) module and distortion-based classification (DC) module. Given a test image and an IQA database, the SC module retrieves multiple pristine images based on semantic similarity. The DC module then retrieves instances based on distortion similarity from the distorted images that correspond to each retrieved pristine image. Finally, the predicted quality score is derived by aggregating the subjective quality scores of multiple retrieved instances. Experimental results on four benchmark databases validate that the proposed model can remarkably outperform the state-of-the-art regression-based models.

GNNInterpreter: A Probabilistic Generative Model-Level Explanation for Graph Neural Networks

Recently, Graph Neural Networks (GNNs) have significantly advanced the performance of machine learning tasks on graphs. However, this technological breakthrough makes people wonder: how does a GNN make such decisions, and can we trust its prediction with high confidence? When it comes to some critical fields such as biomedicine, where making wrong decisions can have severe consequences, interpreting the inner working mechanisms of GNNs before applying them is crucial. In this paper, we propose a novel model-agnostic model-level explanation method for different GNNs that follow the message passing scheme, GNNInterpreter, to explain the high-level decision-making process of the GNN model. More specifically, with continuous relaxation of graphs and the reparameterization trick, GNNInterpreter learns a probabilistic generative graph distribution which produces the most representative graph for the target prediction in the eye of the GNN model. Compared with the only existing work, GNNInterpreter is more computationally efficient and more flexible in generating explanation graphs with different types of node features and edge features, without introducing another blackbox to explain the GNN and without requiring domain-specific knowledge. Additionally, the experimental studies conducted on four different datasets demonstrate that the explanation graph generated by GNNInterpreter can match the desired graph pattern when the model is ideal and reveal potential model pitfalls if there exist any.

SmartGD: A Self-Challenging Generative Adversarial Network for Graph Drawing

A multitude of studies have been conducted on graph drawing, but many existing methods only focus on optimizing particular aesthetic aspects of graph layout. Given a graph, generating a good layout that satisfies certain human aesthetic preference remains a challenging task, especially if such preference can not be expressed as a differentiable objective function. In this paper, we propose a student-teacher GAN-based graph drawing framework, SmartGD, which learns to draw graphs just like how humans learn to perform tasks. The student network in the SmartGD learns graph drawing by imitating good layout examples, while the teacher network in SmartGD is responsible for providing ratings regarding the goodness of the generated layouts. When there is a lack of concrete aesthetic criteria to specify what constitutes a good layout, the student network can learn from the good layout examples. On the other hand, when the goodness of a layout can be assessed by quantitative criteria (even if not differentiable), the student network can use it as a concrete goal to optimize the target aesthetics. To accomplish the goal, we propose a novel variant of GAN, self-challenging GAN, to learn the optimal layout distribution with respect to any aesthetic criterion, whether the criterion is differentiable or not. The proposed graph drawing framework can not only draw graphs in a similar style as the good layout examples but also optimize the graph layouts according to any given aesthetic criteria when available. Once the model is trained, it can be used to visualize arbitrary graphs according to the style of the example layouts or the chosen aesthetic criteria. The comprehensive experimental studies show that SmartGD outperforms 12 benchmark methods according to the commonly agreed metrics.

PrEF: Percolation-based Evolutionary Framework for the diffusion-source-localization problem in large networks

We assume that the state of a number of nodes in a network could be investigated if necessary, and study what configuration of those nodes could facilitate a better solution for the diffusion-source-localization (DSL) problem. In particular, we formulate a candidate set which contains the diffusion source for sure, and propose the method, Percolation-based Evolutionary Framework (PrEF), to minimize such set. Hence one could further conduct more intensive investigation on only a few nodes to target the source. To achieve that, we first demonstrate that there are some similarities between the DSL problem and the network immunization problem. We find that the minimization of the candidate set is equivalent to the minimization of the order parameter if we view the observer set as the removal node set. Hence, PrEF is developed based on the network percolation and evolutionary algorithm. The effectiveness of the proposed method is validated on both model and empirical networks in regard to varied circumstances. Our results show that the developed approach could achieve a much smaller candidate set compared to the state of the art in almost all cases. Meanwhile, our approach is also more stable, i.e., it has similar performance irrespective of varied infection probabilities, diffusion models, and outbreak ranges. More importantly, our approach might provide a new framework to tackle the DSL problem in extreme large networks.

Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery

Self-supervised representation learning (SSL) on biomedical networks provides new opportunities for drug discovery which is lack of available biological or clinic phenotype. However, how to effectively combine multiple SSL models is challenging and rarely explored. Therefore, we propose multi-task joint strategies of self-supervised representation learning on biomedical networks for drug discovery, named MSSL2drug. We design six basic SSL tasks that are inspired by various modality features including structures, semantics, and attributes in biomedical heterogeneous networks. In addition, fifteen combinations of multiple tasks are evaluated by a graph attention-based adversarial multi-task learning framework in two drug discovery scenarios. The results suggest two important findings. (1) The combinations of multimodal tasks achieve the best performance compared to other multi-task joint strategies. (2) The joint training of local and global SSL tasks yields higher performance than random task combinations. Therefore, we conjecture that the multimodal and local-global combination strategies can be regarded as a guideline for multi-task SSL to drug discovery.