Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Semantic Segmentation in Aerial Imagery via Explicit Pixel-Level Constraints

In recent years, image-level weakly supervised semantic segmentation (WSSS) has developed rapidly in natural scenes due to the easy availability of classification tags. However, limited to complex backgrounds, multi-category scenes, and dense small targets in remote sensing (RS) images, relatively little research has been conducted in this field. To alleviate the impact of the above problems in RS scenes, a self-supervised Siamese network based on an explicit pixel-level constraints framework is proposed, which greatly improves the quality of class activation maps and the positioning accuracy in multi-category RS scenes. Specifically, there are three novel devices in this paper to promote performance to a new level: (a) A pixel-soft classification loss is proposed, which realizes explicit constraints on pixels during the image-level training; (b) A pixel global awareness module, which captures high-level semantic context and low-level pixel spatial information, is constructed to improve the consistency and accuracy of RS object segmentation; (c) A dynamic multi-scale fusion module with a gating mechanism is devised, which enhances feature representation and improves the positioning accuracy of RS objects, particularly on small and dense objects. Experiments on two RS challenge datasets demonstrate that these proposed modules achieve new state-of-the-art results by only using image-level labels, which improve mIoU to 36.79% on iSAID and 45.43% on ISPRS in the WSSS task. To the best of our knowledge, this is the first work to perform image-level WSSS on multi-class RS scenes.
TransDARC: Transformer-based Driver Activity Recognition with Latent Space Feature Calibration

Traditional video-based human activity recognition has experienced remarkable progress linked to the rise of deep learning, but this effect was slower as it comes to the downstream task of driver behavior understanding. Understanding the situation inside the vehicle cabin is essential for Advanced Driving Assistant System (ADAS) as it enables identifying distraction, predicting driver's intent and leads to more convenient human-vehicle interaction. At the same time, driver observation systems face substantial obstacles as they need to capture different granularities of driver states, while the complexity of such secondary activities grows with the rising automation and increased driver freedom. Furthermore, a model is rarely deployed under conditions identical to the ones in the training set, as sensor placements and types vary from vehicle to vehicle, constituting a substantial obstacle for real-life deployment of data-driven models. In this work, we present a novel vision-based framework for recognizing secondary driver behaviours based on visual transformers and an additional augmented feature distribution calibration module. This module operates in the latent feature-space enriching and diversifying the training set at feature-level in order to improve generalization to novel data appearances, (e.g., sensor changes) and general feature quality. Our framework consistently leads to better recognition rates, surpassing previous state-of-the-art results of the public Drive&Act benchmark on all granularity levels. Our code will be made publicly available at https://github.com/KPeng9510/TransDARC.
GuideFormer: Transformers for Image Guided Depth Completion

Depth completion has been widely studied to predict a dense depth image from its sparse measurement and a single color image. However, most state-of-the-art methods rely on static convolutional neural networks (CNNs) which are not flexible enough for capturing the dynamic nature of input contexts. In this paper, we propose GuideFormer, a fully transformer-based architecture for dense depth completion. We first process sparse depth and color guidance images with separate transformer branches to extract hierarchical and complementary token representations. Each branch consists of a stack of self-attention blocks and has key design features to make our model suitable for the task. We also devise an effective token fusion method based on guided-attention mechanism. It explicitly models information flow between the two branches and captures inter-modal dependencies that cannot be obtained from depth or color image alone. These properties allow GuideFormer to enjoy various visual dependencies and recover precise depth values while preserving fine details. We evaluate GuideFormer on the KITTI dataset containing realworld driving scenes and provide extensive ablation studies. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art methods.
Counterfactual explanations for models of code

Machine learning (ML) models play an increasingly prevalent role in many software engineering tasks. However, because most models are now powered by opaque deep neural networks, it can be difficult for developers to understand why the model came to a certain conclusion and how to act upon the model's prediction. Motivated by this problem, this paper explores counterfactual explanations for models of source code. Such counterfactual explanations constitute minimal changes to the source code under which the model "changes its mind". We integrate counterfactual explanation generation to models of source code in a real-world setting. We describe considerations that impact both the ability to find realistic and plausible counterfactual explanations, as well as the usefulness of such explanation to the developers that use the model. In a series of experiments we investigate the efficacy of our approach on three different models, each based on a BERT-like architecture operating over source code.
Contrastive Conditional Neural Processes

Conditional Neural Processes (CNPs) bridge neural net-works with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings. Given a batch of non-i.i.d function instantiations, CNPs are jointly optimized for in-instantiation observation prediction and cross-instantiation meta-representation adaptation within a generative reconstruction pipeline. There can be a challenge in tying together such two targets when the distri-bution of function observations scales to high-dimensional and noisy spaces. Instead, noise contrastive estimation might be able to provide more robust representations by learning distributional matching objectives to combat such inherent limitation of generative models. In light of this, we propose to equip CNPs by 1) aligning prediction with en-coded ground-truth observation, and 2) decoupling meta-representation adaptation from generative reconstruction. Specifically, two auxiliary contrastive branches are set up hierarchically, namely in-instantiation temporal contrastive learning (TCL) and cross-instantiation function contrastive learning (FCL), to facilitate local predictive alignment and global function consistency, respectively. We empirically show that TCL captures high-level abstraction of obser-vations, whereas FCL helps identify underlying functions, which in turn provides more efficient representations. Our model outperforms other CNPs variants when evaluating function distribution reconstruction and parameter identifi-cation across 1D, 2D and high-dimensional time-series.
SAR Despeckling Using Overcomplete Convolutional Networks

Synthetic Aperture Radar (SAR) despeckling is an important problem in remote sensing as speckle degrades SAR images, affecting downstream tasks like detection and segmentation. Recent studies show that convolutional neural networks (CNNs) outperform classical despeckling methods. Traditional CNNs try to increase the receptive field size as the network goes deeper, thus extracting global features. However, speckle is relatively small, and increasing receptive field does not help in extracting speckle features. This study employs an overcomplete CNN architecture to focus on learning low-level features by restricting the receptive field. The proposed network consists of an overcomplete branch to focus on the local structures and an undercomplete branch that focuses on the global structures. We show that the proposed network improves despeckling performance compared to recent despeckling methods on synthetic and real SAR images. Our code is available at: https://github.com/malshaV/sar_overcomplete
A Privacy-Preserving Social Computing Framework for Health Management Using Federated Learning

Currently, health management driven by intelligent means is a general demand of social systems. Although a number of researchers have paid attention to such areas, they have primarily focused on improving the performance of intelligent algorithms. Such intelligent algorithms are mostly based on the central computing mode, where all the user data are aggregated together in a central cloud to implement computing tasks. This poses a great threat to personal privacy due to exposure to the outside world. To address this challenge, this work uses a federated learning mechanism and proposes a privacy-preserving social computing framework for health management. User data are deposited in different user terminals to prevent exposure. A group of parameters are pretrained for each terminal in an iteration and are then transferred to the center cloud for updating. After multiple rounds of interactive training between the center cloud and the terminals, a recognition model finishes training for each terminal without direct access to data from other sources. Finally, this work also conducts experiments on a real-world dataset to assess the overall performance of the proposed approach.
WebQA: Multihop and Multimodal QA

Scaling Visual Question Answering (VQA) to the open-domain and multi-hop nature of web searches, requires fundamental advances in visual representation learning, knowledge aggregation, and language generation. In this work, we introduce WEBQA, a challenging new benchmark that proves difficult for large-scale state-of-the-art models which lack language groundable visual representations for novel objects and the ability to reason, yet trivial for humans. WebQA mirrors the way humans use the web: 1) Ask a question, 2) Choose sources to aggregate, and 3) Produce a fluent language response. This is the behavior we should be expecting from IoT devices and digital assistants. Existing work prefers to assume that a model can either reason about knowledge in images or in text. WebQA includes a secondary text-only QA task to ensure improved visual performance does not come at the cost of language understanding. Our challenge for the community is to create unified multimodal reasoning models that answer questions regardless of the source modality, moving us closer to digital assistants that not only query language knowledge, but also the richer visual online world.
ART-Point: Improving Rotation Robustness of Point Cloud Classifiers via Adversarial Rotation

Point cloud classifiers with rotation robustness have been widely discussed in the 3D deep learning community. Most proposed methods either use rotation invariant descriptors as inputs or try to design rotation equivariant networks. However, robust models generated by these methods have limited performance under clean aligned datasets due to modifications on the original classifiers or input space. In this study, for the first time, we show that the rotation robustness of point cloud classifiers can also be acquired via adversarial training with better performance on both rotated and clean datasets. Specifically, our proposed framework named ART-Point regards the rotation of the point cloud as an attack and improves rotation robustness by training the classifier on inputs with Adversarial RoTations. We contribute an axis-wise rotation attack that uses back-propagated gradients of the pre-trained model to effectively find the adversarial rotations. To avoid model overfitting on adversarial inputs, we construct rotation pools that leverage the transferability of adversarial rotations among samples to increase the diversity of training data. Moreover, we propose a fast one-step optimization to efficiently reach the final robust model. Experiments show that our proposed rotation attack achieves a high success rate and ART-Point can be used on most existing classifiers to improve the rotation robustness while showing better performance on clean datasets than state-of-the-art methods.
Maintaining Reasoning Consistency in Compositional Visual Question Answering

A compositional question refers to a question that contains multiple visual concepts (e.g., objects, attributes, and relationships) and requires compositional reasoning to answer. Existing VQA models can answer a compositional question well, but cannot work well in terms of reasoning consistency in answering the compositional question and its sub-questions. For example, a compositional question for an image is: “Are there any elephants to the right of the white bird?” and one of its sub-questions is “Is any bird visible in the scene?”. The models may answer “yes” to the compositional question, but “no” to the sub-question. This paper presents a dialog-like reasoning method for maintaining reasoning consistency in answering a compositional question and its sub-questions. Our method integrates the reasoning processes for the sub-questions into the reasoning process for the compositional question like a dialog task, and uses a consistency constraint to penalize inconsistent answer predictions. In order to enable quantitative evaluation of reasoning consistency, we construct a GQA-Sub dataset based on the well-organized GQA dataset. Experimental results on the GQA dataset and the GQA-Sub dataset demonstrate the effectiveness of our method.
OrphicX: A Causality-Inspired Latent Variable Model for Interpreting Graph Neural Networks

This paper proposes a new eXplanation framework, called OrphicX, for generating causal explanations for any graph neural networks (GNNs) based on learned latent causal factors. Specifically, we construct a distinct generative model and design an objective function that encourages the generative model to produce causal, compact, and faithful explanations. This is achieved by isolating the causal factors in the latent space of graphs by maximizing the information flow measurements. We theoretically analyze the cause-effect relationships in the proposed causal graph, identify node attributes as confounders between graphs and GNN predictions, and circumvent such confounder effect by leveraging the backdoor adjustment formula. Our framework is compatible with any GNNs, and it does not require access to the process by which the target GNN produces its predictions. In addition, it does not rely on the linear-independence assumption of the explained features, nor require prior knowledge on the graph learning tasks. We show a proof-of-concept of OrphicX on canonical classification problems on graph data. In particular, we analyze the explanatory subgraphs obtained from explanations for molecular graphs (i.e., Mutag) and quantitatively evaluate the explanation performance with frequently occurring subgraph patterns. Empirically, we show that OrphicX can effectively identify the causal semantics for generating causal explanations, significantly outperforming its alternatives <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This project is supported by the Internal Research Fund at The Hong Kong Polytechnic University P0035763. HW is partially supported by NSF Grant IIS-2127918 and an Amazon Faculty Research Award..
Navigating to Objects in Unseen Environments by Distance Prediction

Object Goal Navigation (ObjectNav) task is to navigate an agent to an object category in unseen environments without a pre-built map. In this paper, we solve this task by predicting the distance to the target using semantically-related objects as cues. Based on the estimated distance to the target object, our method directly choose optimal midterm goals that are more likely to have a shorter path to the target. Specifically, based on the learned knowledge, our model takes a bird's-eye view semantic map as input, and estimates the path length from the frontier map cells to the target object. With the estimated distance map, the agent could simultaneously explore the environment and navigate to the target objects based on a simple human-designed strategy. Empirical results in visually realistic simulation environments show that the proposed method outperforms a wide range of baselines on success rate and efficiency. Real-robot experiment also demonstrates that our method generalizes well to the real world.
MinPlot: A mineral formula recalculation and plotting program for electron probe microanalysis

Abstract MinPlot is a MATLAB®-based mineral formula recalculation and compositional plotting program for electron microprobe analyses (EPMA). The program offers recalculation and structural formula assignment for 15 different mineral groups: Garnet, pyroxene, olivine, amphibole, feldspar, mica, staurolite, cordierite, chlorite, chloritoid, talc, epidote, titanite, spinel, and sulfides. MinPlot is a fast and easy to use command line program and requires no prior computer programming knowledge. Percent mass fractions of oxides are loaded from datafiles and the user answers simple prompts to select mineral type, normalization scheme, and plotting options. Recalculated mineral formulas are automatically saved as output files and plots may be further manually customized by the user prior to saving. MinPlot can perform thousands of calculations in seconds and the modular nature of the program makes it simple to add new calculation routines in future releases. Combined, these features make MinPlot a powerful and useful program for the processing of EPMA data.
PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation

In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360° panoramas. Our system, called Position-aware Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator. PSMNet consists of a Stereo Pano Pose (SP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) transformer and a novel Cross-Perspective Projection (CP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) layer. The stereo-view SP2 transformer is used to implicitly infer correspondences between views, and can handle noisy poses. The pose-aware CP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> layer is designed to render features from the adjacent view to the anchor (reference) view, in order to perform view fusion and estimate the visible layout. Our experiments and analysis validate our method, which significantly outperforms the state-of-the-art layout estimators, especially for large and complex room spaces.
Audio-driven Neural Gesture Reenactment with Video Motion Graphs

Human speech is often accompanied by body gestures including arm and hand gestures. We present a method that reenacts a high-quality video with gestures matching a target speech audio. The key idea of our method is to split and re-assemble clips from a reference video through a novel video motion graph encoding valid transitions between clips. To seamlessly connect different clips in the reenactment, we propose a pose-aware video blending network which synthesizes video frames around the stitched frames between two clips. Moreover, we developed an audio-based gesture searching algorithm to find the optimal order of the reenacted frames. Our system generates reen-actments that are consistent with both the audio rhythms and the speech content. We evaluate our synthesized video quality quantitatively, qualitatively, and with user studies, demonstrating that our method produces videos of much higher quality and consistency with the target audio compared to previous work and baselines. Our project page https://github.com/yzhou359/vid-reenact includes code and data.
DeepFake Disrupter: The Detector of DeepFake Is My Friend

In recent years, with the advances of generative models, many powerful face manipulation systems have been developed based on Deep Neural Networks (DNNs), called DeepFakes. If DeepFakes are not controlled timely and properly, they would become a real threat to both celebrities and ordinary people. Precautions such as adding perturbations to the source inputs will make DeepFake results look distorted from the perspective of human eyes. However, previous method doesn't explore whether the disrupted images can still spoof DeepFake detectors. This is critical for many applications where DeepFake detectors are used to discriminate between DeepFake data and real data due to the huge cost of examining a large amount of data manually. We argue that the detectors do not share a similar perspective as human eyes, which might still be spoofed by the disrupted data. Besides, the existing disruption methods rely on iteration-based perturbation generation algorithms, which is time-consuming. In this paper, we propose a novel DeepFake disruption algorithm called “DeepFake Disrupter”. By training a perturbation generator, we can add the human-imperceptible perturbations to source images that need to be protected without any backpropagation update. The DeepFake results of these protected source inputs would not only look unrealistic by the human eye but also can be distinguished by DeepFake detectors easily. For example, experimental results show that by adding our trained perturbations, fake images generated by StarGAN [5] can result in a 10 ∼ 20% increase in F1-score evaluated by various DeepFake detectors.
Knowledge Mining with Scene Text for Fine-Grained Recognition

Recently, the semantics of scene text has been proven to be essential in fine-grained image classification. However, the existing methods mainly exploit the literal meaning of scene text for fine-grained recognition, which might be irrelevant when it is not significantly related to objects/scenes. We propose an end-to-end trainable network that mines implicit contextual knowledge behind scene text image and enhance the semantics and correlation to fine-tune the image representation. Unlike the existing methods, our model integrates three modalities: visual feature extraction, text semantics extraction, and correlating background knowledge to fine-grained image classification. Specifically, we employ KnowBert to retrieve relevant knowledge for semantic representation and combine it with image features for fine-grained classification. Experiments on two benchmark datasets, Con-Text, and Drink Bottle, show that our method outperforms the state-of-the-art by 3.72% mAP and 5.39% mAp, respectively. To further validate the effectiveness of the proposed method, we create a new dataset on crowd activity recognition for the evaluation. The source code and new dataset of this work are available at this repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/lanfeng4659/KnowledgeMiningWithSceneText.