Task Residual for Tuning Vision-Language Models

Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the well-learned knowledge structure of the VLMs should be inherited appropriately when being transferred to downstream tasks with limited data. However, most existing efficient transfer learning (ETL) approaches for VLMs either damage or are excessively biased towards the prior knowledge, e.g., prompt tuning (PT) discards the pre-trained text-based classifier and builds a new one while adapter-style tuning (AT) fully relies on the pre-trained features. To address this, we propose a new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the text-based classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. Specifically, TaskRes keeps the original classifier weights from the VLMs frozen and obtains a new classifier for the target task by tuning a set of prior-independent parameters as a residual to the original one, which enables reliable prior knowledge preservation and flexible task-specific knowledge exploration. The proposed TaskRes is simple yet effective, which significantly outperforms previous ETL methods (e.g., PT and AT) on 11 benchmark datasets while requiring minimal effort for the implementation. Our code is available at https://github.com/geekyutao/TaskRes.Comment: Accepted to CVPR 202

Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation

Morphable models are essential for the statistical modeling of 3D faces. Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details. This paper augments morphable models in representing facial details by learning a Structure-aware Editable Morphable Model (SEMM). SEMM introduces a detail structure representation based on the distance field of wrinkle lines, jointly modeled with detail displacements to establish better correspondences and enable intuitive manipulation of wrinkle structure. Besides, SEMM introduces two transformation modules to translate expression blendshape weights and age values into changes in latent space, allowing effective semantic detail editing while maintaining identity. Extensive experiments demonstrate that the proposed model compactly represents facial details, outperforms previous methods in expression animation qualitatively and quantitatively, and achieves effective age editing and wrinkle line editing of facial details. Code and model are available at https://github.com/gerwang/facial-detail-manipulation.Comment: ECCV 202

GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph

Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning of vision-language models (VLMs) under the low-data regime, where only a few additional parameters are introduced to excavate the task-specific knowledge based on the general and powerful representation of VLMs. However, most adapter-style works face two limitations: (i) modeling task-specific knowledge with a single modality only; and (ii) overlooking the exploitation of the inter-class relationships in downstream tasks, thereby leading to sub-optimal solutions. To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dual-modality structure knowledge (i.e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph. In particular, the dual knowledge graph is established with two sub-graphs, i.e., a textual knowledge sub-graph, and a visual knowledge sub-graph, where the nodes and edges represent the semantics/classes and their correlations in two modalities, respectively. This enables the textual feature of each prompt to leverage the task-specific structure knowledge from both textual and visual modalities, yielding a more effective classifier for downstream tasks. Extensive experimental results on 11 benchmark datasets reveal that our GraphAdapter significantly outperforms previous adapter-based methods. The code will be released at https://github.com/lixinustc/GraphAdapterComment: Accepted by NeurIPS 2023. The manuscript will be further revised based on the review

OptScaler: A Hybrid Proactive-Reactive Framework for Robust Autoscaling in the Cloud

Autoscaling is a vital mechanism in cloud computing that supports the autonomous adjustment of computing resources under dynamic workloads. A primary goal of autoscaling is to stabilize resource utilization at a desirable level, thus reconciling the need for resource-saving with the satisfaction of Service Level Objectives (SLOs). Existing proactive autoscaling methods anticipate the future workload and scale the resources in advance, whereas the reliability may suffer from prediction deviations arising from the frequent fluctuations and noise of cloud workloads; reactive methods rely on real-time system feedback, while the hysteretic nature of reactive methods could cause violations of the rigorous SLOs. To this end, this paper presents OptScaler, a hybrid autoscaling framework that integrates the power of both proactive and reactive methods for regulating CPU utilization. Specifically, the proactive module of OptScaler consists of a sophisticated workload prediction model and an optimization model, where the former provides reliable inputs to the latter for making optimal scaling decisions. The reactive module provides a self-tuning estimator of CPU utilization to the optimization model. We embed Model Predictive Control (MPC) mechanism and robust optimization techniques into the optimization model to further enhance its reliability. Numerical results have demonstrated the superiority of both the workload prediction model and the hybrid framework of OptScaler in the scenario of online services compared to prevalent reactive, proactive, or hybrid autoscalers. OptScaler has been successfully deployed at Alipay, supporting the autoscaling of applets in the world-leading payment platform

Spontaneous breaking and re-making of the RS-Au-SR staple in self-assembled ethylthiolate/Au(111) interface

The stability of the self-assembled RS–Au–SR (R = CH₂CH₃)/Au­(111) interface at room temperature has been investigated using scanning tunneling microscopy (STM) in conjunction with density functional theory (DFT) and MD calculations. The RS–Au–SR staple, also known as Au-adatom-dithiolate, assembles into staple rows along the [112̅] direction. STM imaging reveals that while the staple rows are able to maintain a static global structure, individual staples within the row are subjected to constant breaking and remaking of the Au–SR bond. The C₂S–Au–SC₂/Au­(111) interface is under a dynamic equilibrium and it is far from rigid. DFT/MD calculations show that a transient RS–Au–Au–SR complex can be formed when a free Au atom is added to the RS–Au–SR staple. The relatively high reactivity of the RS–Au–SR staple at room temperature could explain the reactivity of thiolate-protected Au nanoclusters, such as their ability to participate in ligand exchange and intercluster reactions

PACE Solver Description: Hust-Solver - A Heuristic Algorithm of Directed Feedback Vertex Set Problem

A directed graph is formed by vertices and arcs from one vertex to another. The feedback vertex set problem (FVSP) consists in making a given directed graph acyclic by removing as few vertices as possible. In this write-up, we outline the core techniques used in the heuristic feedback vertex set algorithm, submitted to the heuristic track of the 2022 PACE challenge

Astragaloside IV alleviates 1-deoxysphinganine-induced mitochondrial dysfunction during the progression of chronic kidney disease through p62-Nrf2 antioxidant pathway

Introduction: Chronic kidney disease (CKD) can lead to significant elevation of 1-deoxysphingolipids (1-deoxySL). The increase of 1-deoxySL in turn can result in mitochondrial damage and oxidative stress, which can cause further progression of CKD.Methods: This study assessed the therapeutic effect of Astragaloside IV (AST) against 1-deoxySL-induced cytotoxicity in vitro and in rats with CKD. HK-2 cells were exposed to 1-deoxysphinganine (doxSA) or doxSA + AST. doxSA-induced mitochondrial dysfunction and oxidative stress were evaluated by immunostaining, real-time PCR, oxidative stress sensor, and transmission electron microscopy. The potential effects of AST on kidney damage were evaluated in a rat 5/6 nephrectomy (5/6 Nx) model of CKD.Results: The findings of in vitro experiments showed that doxSA induced mitochondrial damage, oxidative stress, and apoptosis. AST markedly reduced the level of mitochondrial reactive oxygen species, lowered apoptosis, and improved mitochondrial function. In addition, exposure to AST significantly induced the phosphorylation of p62 and the nuclear translocation of Nrf2 as well as its downstream anti-oxidant genes. p62 knock-down fully abolished Nrf2 nuclear translocation in cells after AST treatment. However, p62 knock-down did not affect TBHQ-induced Nrf2 nuclear translocation, indicating that AST can ameliorate doxSA-induced oxidative stress through modulation of p62 phosphorylation and Nrf2 nuclear translocation.Conclusion: The findings indicate that AST can activate Nrf2 antioxidant pathway in a p62 dependent manner. The anti-oxidative stress effect and the further mitochondrial protective effect of AST represent a promising therapeutic strategy for the progression of CKD