Improving Scene Graph Generation with Superpixel-Based Interaction Learning

Recent advances in Scene Graph Generation (SGG) typically model the relationships among entities utilizing box-level features from pre-defined detectors. We argue that an overlooked problem in SGG is the coarse-grained interactions between boxes, which inadequately capture contextual semantics for relationship modeling, practically limiting the development of the field. In this paper, we take the initiative to explore and propose a generic paradigm termed Superpixel-based Interaction Learning (SIL) to remedy coarse-grained interactions at the box level. It allows us to model fine-grained interactions at the superpixel level in SGG. Specifically, (i) we treat a scene as a set of points and cluster them into superpixels representing sub-regions of the scene. (ii) We explore intra-entity and cross-entity interactions among the superpixels to enrich fine-grained interactions between entities at an earlier stage. Extensive experiments on two challenging benchmarks (Visual Genome and Open Image V6) prove that our SIL enables fine-grained interaction at the superpixel level above previous box-level methods, and significantly outperforms previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing box-level approaches in a plug-and-play fashion. In particular, SIL brings an average improvement of 2.0% mR (even up to 3.4%) of baselines for the PredCls task on Visual Genome, which facilitates its integration into any existing box-level method

A Generic Multi-Player Transformation Algorithm for Solving Large-Scale Zero-Sum Extensive-Form Adversarial Team Games

Many recent practical and theoretical breakthroughs focus on adversarial team multi-player games (ATMGs) in ex ante correlation scenarios. In this setting, team members are allowed to coordinate their strategies only before the game starts. Although there existing algorithms for solving extensive-form ATMGs, the size of the game tree generated by the previous algorithms grows exponentially with the number of players. Therefore, how to deal with large-scale zero-sum extensive-form ATMGs problems close to the real world is still a significant challenge. In this paper, we propose a generic multi-player transformation algorithm, which can transform any multi-player game tree satisfying the definition of AMTGs into a 2-player game tree, such that finding a team-maxmin equilibrium with correlation (TMECor) in large-scale ATMGs can be transformed into solving NE in 2-player games. To achieve this goal, we first introduce a new structure named private information pre-branch, which consists of a temporary chance node and coordinator nodes and aims to make decisions for all potential private information on behalf of the team members. We also show theoretically that NE in the transformed 2-player game is equivalent TMECor in the original multi-player game. This work significantly reduces the growth of action space and nodes from exponential to constant level. This enables our work to outperform all the previous state-of-the-art algorithms in finding a TMECor, with 182.89, 168.47, 694.44, and 233.98 significant improvements in the different Kuhn Poker and Leduc Poker cases (21K3, 21K4, 21K6 and 21L33). In addition, this work first practically solves the ATMGs in a 5-player case which cannot be conducted by existing algorithms.Comment: 9 pages, 5 figures, NIPS 202

Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition

Graph convolutional networks have been widely applied in skeleton-based gait recognition. A key challenge in this task is to distinguish the individual walking styles of different subjects across various views. Existing state-of-the-art methods employ uniform convolutions to extract features from diverse sequences and ignore the effects of viewpoint changes. To overcome these limitations, we propose a condition-adaptive graph (CAG) convolution network that can dynamically adapt to the specific attributes of each skeleton sequence and the corresponding view angle. In contrast to using fixed weights for all joints and sequences, we introduce a joint-specific filter learning (JSFL) module in the CAG method, which produces sequence-adaptive filters at the joint level. The adaptive filters capture fine-grained patterns that are unique to each joint, enabling the extraction of diverse spatial-temporal information about body parts. Additionally, we design a view-adaptive topology learning (VATL) module that generates adaptive graph topologies. These graph topologies are used to correlate the joints adaptively according to the specific view conditions. Thus, CAG can simultaneously adjust to various walking styles and viewpoints. Experiments on the two most widely used datasets (i.e., CASIA-B and OU-MVLP) show that CAG surpasses all previous skeleton-based methods. Moreover, the recognition performance can be enhanced by simply combining CAG with appearance-based methods, demonstrating the ability of CAG to provide useful complementary information.The source code will be available at https://github.com/OliverHxh/CAG.Comment: Accepted by TIP journa

TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Project page: https://vcc.tech/research/2023/TwinTex.Comment: Accepted to SIGGRAPH ASIA 202

Context-Sensitive Temporal Feature Learning for Gait Recognition

Although gait recognition has drawn increasing research attention recently, it remains challenging to learn discriminative temporal representation, since the silhouette differences are quite subtle in spatial domain. Inspired by the observation that human can distinguish gaits of different subjects by adaptively focusing on temporal clips with different time scales, we propose a context-sensitive temporal feature learning (CSTL) network for gait recognition. CSTL produces temporal features in three scales, and adaptively aggregates them according to the contextual information from local and global perspectives. Specifically, CSTL contains an adaptive temporal aggregation module that subsequently performs local relation modeling and global relation modeling to fuse the multi-scale features. Besides, in order to remedy the spatial feature corruption caused by temporal operations, CSTL incorporates a salient spatial feature learning (SSFL) module to select groups of discriminative spatial features. Particularly, we utilize transformers to implement the global relation modeling and the SSFL module. To the best of our knowledge, this is the first work that adopts transformer in gait recognition. Extensive experiments conducted on three datasets demonstrate the state-of-the-art performance. Concretely, we achieve rank-1 accuracies of 98.7%, 96.2% and 88.7% under normal-walking, bag-carrying and coat-wearing conditions on CASIA-B, 97.5% on OU-MVLP and 50.6% on GREW.Comment: Submitted to TPAM

Non-covalent interactions in electrochemical reactions and implications in clean energy applications

Understanding and controlling non-covalent interactions associated with solvent molecules and redox-inactive ions provide new opportunities to enhance the reaction entropy changes and reaction kinetics of metal redox centers, which can increase the thermodynamic efficiency of energy conversion and storage devices. Here, we report systematic changes in the redox entropy of one-electron transfer reactions including [Fe(CN)6]3-/4-, [Fe(H2O)6]3+/2+and [Ag(H2O)4]+/0induced by the addition of redox inactive ions, where approximately twenty different known structure making/breaking ions were employed. The measured reaction entropy changes of these redox couples were found to increase linearly with higher concentration and greater structural entropy (having greater structure breaking tendency) for inactive ions with opposite charge to the redox centers. The trend could be attributed to the altered solvation shells of oxidized and reduced redox active species due to non-covalent interactions among redox centers, inactive ions and water molecules, which was supported by Raman spectroscopy. Not only were these non-covalent interactions shown to increase reaction entropy, but they were also found to systematically alter the redox kinetics, where increasing redox reaction energy changes associated with the presence of water structure breaking cations were correlated linearly with the greater exchange current density of [Fe(CN)6]3-/4-.United States. Department of Energy. Office of Basic Energy Science (Award Number DE-SC0001299/DE-FG02-09ER46577)Hong Kong (China). Innovation and Technology Commission (Project No. ITS/ 020/16FP)United States. Department of Energy (Contract No. DE-AC02-5CH11231

Discovery of BRAF/HDAC Dual Inhibitors Suppressing Proliferation of Human Colorectal Cancer Cells

The combination of histone deacetylase inhibitor and BRAF inhibitor (BRAFi) has been shown to enhance the antineoplastic effect and reduce the progress of BRAFi resistance. In this study, a series of (thiazol-5-yl)pyrimidin-2-yl)amino)-N-hydroxyalkanamide derivatives were designed and synthesized as novel dual inhibitors of BRAF and HDACs using a pharmacophore hybrid strategy. In particular, compound 14b possessed potent activities against BRAF, HDAC1, and HDAC6 enzymes. It potently suppressed the proliferation of HT-29 cells harboring BRAFV600E mutation as well as HCT116 cells with wild-type BRAF. The dual inhibition against BRAF and HDAC downstream proteins was validated in both cells. Collectively, the results support 14b as a promising lead molecule for further development and a useful tool for studying the effects of BRAF/HDAC dual inhibitors

Mécanismes de vieillissement de l'Assemblage-Membrane-Électrodes dans une pile à combustible de type PEM par approche expérimentale

This thesis highlights the aging mechanisms of PEM Fuel Cell submitted to two main aging conditions: air relative humidity (RH) cycling, and MEA (Membrane Electrode Assembly) pinhole test of operation. First, the aging mechanisms of PEMFC main components (membrane, catalyst, carbon support, GDL, bipolar plates and gaskets), have been reviewed from the literature. Then the on-line diagnostic tools (chronopotentiometry, electrochemical impedance spectroscopy, water management and water analysis), off-line ones (cyclic voltammetry and linear sweep voltammetry) and post-mortem analyses (nuclear magnetic resonance, transmission electron microscopy, scanning electron microscopy and X-ray diffraction) have been described. Experimentally, the high and low air RH cycling runs have been carried out with a 25 cm2 single cell: the high air RH cycling run promoted serious loss of the ElectroChemical Surface Area (ECSA); the low air RH cycling run caused significant increase in hydrogen crossover. The low air RH cycling has been also performed with a 100 cm2 single cell and the aging mechanism was different from that of 25 cm2 cell: the hydrogen crossover remained very low but the fuel cell voltage exhibited strong fluctuations at the end of the run: this was attributed to the presence of dead volumes and liquid water retention within the cell. Finally, MEA pinhole effect has been investigated with a 100 cm2 single cell: the perforation by a 0.7 mm diameter pin promoted slight increase in the hydrogen crossover; the perforation by a 1.2 mm diameter pin caused significant cell voltage losses and serious increase in the cathode diffusion resistance due to significant hydrogen crossoverCette thèse a permis de mettre en évidence les mécanismes de vieillissement de la pile à combustible de type PEM lors de cyclages d'humidité de l'air et suite à la perforation de l'AME (Assemblage Membrane Electrodes). Premièrement, les mécanismes connus de dégradation des divers composants (membrane, catalyseur, support du catalyseur, GDL, plaques bipolaires et joints d'étanchéité) ont été présentés. Ensuite, les outils de diagnostic en-ligne (chronopotentiométrie, spectroscopie d'impédance, gestion de l'eau et analyse chimique de l'eau) et ceux hors-ligne (CV et LSV) ainsi que des analyses post-mortem (RMN, MET, MEB et DRX) ont été décrits. Expérimentalement, le cyclage en humidité de l'air a été effectué en mono-cellule de 25 cm2: le cyclage à forte humidité entraîne une perte significative de la surface électroactive du catalyseur; le cyclage à faible humidité favorise la perméation de l'hydrogène à travers la membrane. Le cyclage à faible humidité réalisé sur une pile de 100 cm2 a montré un mécanisme de dégradation différent de celui de la pile de 25 cm2: la perméation de l'hydrogène reste faible alors que la tension de la pile était de plus en plus fluctuante certainement du fait de la présence de volumes morts et de la rétention d'eau liquide dans la pile. L'effet de la perforation de l'AME a été étudié sur une pile de 100 cm2: la perforation par une punaise de 0,7 mm de diamètre ne génère qu'une légère augmentation de la perméation de l'hydrogène; la perforation par une punaise de 1,2 mm de diamètre entraîne une chute de tension et l'augmentation significative de la résistance de diffusion de l'oxygène due à la perméation importante de l'hydrogèn