Reviewerly/exHarmony huggingface dataset:
Task-Adaptive Negative Envision for Few-Shot Open-Set Recognition

We study the problem of few-shot open-set recognition (FSOR), which learns a recognition system capable of both fast adaptation to new classes with limited labeled exam-ples and rejection of unknown negative samples. Traditional large-scale open-set methods have been shown in-effective for FSOR problem due to data limitation. Current FSOR methods typically calibrate few-shot closed-set clas-sifiers to be sensitive to negative samples so that they can be rejected via thresholding. However, threshold tuning is a challenging process as different FSOR tasks may require different rejection powers. In this paper, we instead propose task-adaptive negative class envision for FSOR to integrate threshold tuning into the learning process. Specifically, we augment the few-shot closed-set classifier with additional negative prototypes generated from few-shot examples. By incorporating few-shot class correlations in the negative generation process, we are able to learn dynamic rejection boundaries for FSOR tasks. Besides, we extend our method to generalized few-shot open-set recognition (GF-SOR), which requires classification on both many-shot and few-shot classes as well as rejection of negative samples. Extensive experiments on public benchmarks validate our methods on both problems. <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> Code available at https://github.com/shiyuanh/TANE
Multi-View Clustering Based on Multiple Manifold Regularized Non-Negative Sparse Matrix Factorization

Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is composed through different domain which shows the consistent and complementary behavior. The existing studies did not draw attention of over-fitting and sparsity among the diverse view, which is the considerable issue for getting the unique consensus knowledge from these complementary data. Herein article, a multi-view clustering approach is recommended to provide the consensus solution from the multi-view data. To accomplish this task, we exploit non-negative matrix factorized method to generate a cost function. Further, manifold learning model is used to build the graph through the nearest neighbor strategy, which is effective to save the geometrical design for data and feature matrix. Furthermore, the over-fitting problem, sparsity is handled through adaption of frobenious norm, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> -norm on basis and coefficient matrices. The whole formulation is done through the mathematical function, which is optimized through the iterative updating strategy to get the optimal solution. The computational experiment is carried on the available datasets to exhibits that the proposed strategy beats the current methodologies in terms of clustering execution.
A lightweight multi-scale context network for salient object detection in optical remote sensing images

Due to the more dramatic multi-scale variations and more complicated foregrounds and backgrounds in optical remote sensing images (RSIs), the salient object detection (SOD) for optical RSIs becomes a huge challenge. However, different from natural scene images (NSIs), the discussion on the optical RSI SOD task still remains scarce. In this paper, we propose a multi-scale context network, namely MSCNet, for SOD in optical RSIs. Specifically, a multi-scale context extraction module is adopted to address the scale variation of salient objects by effectively learning multi-scale contextual information. Meanwhile, in order to accurately detect complete salient objects in complex backgrounds, we design an attention-based pyramid feature aggregation mechanism for gradually aggregating and refining the salient regions from the multi-scale context extraction module. Extensive experiments on two benchmarks demonstrate that MSCNet achieves competitive performance with only 3.26M parameters. The code will be available at https://github.com/NuaaYH/MSCNet.
Pareto Refocusing for Drone-View Object Detection

Drone-view Object Detection (DOD) is a meaningful but challenging task. It hits a bottleneck due to two main reasons: (1) The high proportion of difficult objects (e.g., small objects, occluded objects, etc.) makes the detection performance unsatisfactory. (2) The unevenly distributed objects make detection inefficient. These two factors also lead to a phenomenon, obeying the Pareto principle, that some challenging regions occupying a low area proportion of the image have a significant impact on the final detection while the vanilla regions occupying the major area have a negligible impact due to the limited room for performance improvement. Motivated by the human visual system that naturally attempts to invest unequal energies in things of hierarchical difficulty for recognizing objects effectively, this paper presents a novel Pareto Refocusing Detection (PRDet) network that distinguishes the challenging regions from the vanilla regions under reverse-attention guidance and refocuses the challenging regions with the assistance of the region-specific context. Specifically, we first propose a Reverse-attention Exploration Module (REM) that excavates the potential position of difficult objects by suppressing the features which are salient to the commonly used detector. Then, we propose a Region-specific Context Learning Module (RCLM) that learns to generate specific contexts for strengthening the understanding of challenging regions. It is noteworthy that the specific context is not shared globally but unique for each challenging region with the exploration of spatial and appearance cues. Extensive experiments and comprehensive evaluations on the VisDrone2021-DET and UAVDT datasets demonstrate that the proposed PRDet can effectively improve the detection performance, especially for those difficult objects, outperforming state-of-the-art detectors. Furthermore, our method also achieves significant performance improvements on the DTU-Drone dataset for power inspection.
Quantifying Societal Bias Amplification in Image Captioning

We study societal bias amplification in image captioning. Image captioning models have been shown to perpetuate gender and racial biases, however, metrics to measure, quantify, and evaluate the societal bias in captions are not yet standardized. We provide a comprehensive study on the strengths and limitations of each metric, and propose LIC, a metric to study captioning bias amplification. We argue that, for image captioning, it is not enough to focus on the correct prediction of the protected attribute, and the whole context should be taken into account. We conduct extensive evaluation on traditional and state-of-the-art image captioning models, and surprisingly find that, by only focusing on the protected attribute prediction, bias mitigation models are unexpectedly amplifying bias.
Wireless Distributed Learning: A New Hybrid Split and Federated Learning Approach

Cellular-connected unmanned aerial vehicle (UAV) with flexible deployment is foreseen to be a major part of the sixth generation (6G) networks. The UAVs connected to the base station (BS), as aerial users (UEs), could exploit machine learning (ML) algorithms to provide a wide range of advanced applications, like object detection and video tracking. Conventionally, the ML model training is performed at the BS, known as centralized learning (CL), which causes high communication overhead due to the transmission of large datasets, and potential concerns about UE privacy. To address this, distributed learning algorithms, including federated learning (FL) and split learning (SL), were proposed to train the ML models in a distributed manner via only sharing model parameters. FL requires higher computational resource on the UE side than SL, while SL has larger communication overhead when the local dataset is large. To effectively train an ML model considering the diversity of UEs with different computational capabilities and channel conditions, we first propose a novel distributed learning architecture, a hybrid split and federated learning (HSFL) algorithm by reaping the parallel model training mechanism of FL and the model splitting structure of SL. We then provide its convergence analysis under non-independent and identically distributed (non-IID) data with random UE selection scheme. By conducting experiments on training two ML models, Net and AlexNet, in wireless UAV networks, our results demonstrate that the HSFL algorithm achieves higher learning accuracy than FL and less communication overhead than SL under IID and non-IID data, and the learning accuracy of HSFL algorithm increases with the increasing number of the split training UEs. We further propose a Multi-Arm Bandit (MAB) based best channel (BC) and best 2-norm (BN2) (MAB-BC-BN2) UE selection scheme to select the UEs with better wireless channel quality and larger local model updates for model training in each round. Numerical results demonstrate it achieves higher learning accuracy than BC, MAB-BC and MAB-BN2 UE selection scheme under non-IID, Dirichlet-nonIID and Dirichlet-Imbalanced data.
BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning

Deep neural networks are vulnerable to Trojan attacks. Existing attacks use visible patterns (e.g., a patch or image transformations) as triggers, which are vulnerable to human inspection. In this paper, we propose stealthy and efficient Trojan attacks, BppAttack. Based on existing biology literature on human visual systems, we propose to use image quantization and dithering as the Trojan trigger, making imperceptible changes. It is a stealthy and efficient attack without training auxiliary models. Due to the small changes made to images, it is hard to inject such triggers during training. To alleviate this problem, we propose a contrastive learning based approach that leverages adversarial attacks to generate negative sample pairs so that the learned trigger is precise and accurate. The proposed method achieves high attack success rates on four benchmark datasets, including MNIST, CIFAR-10, GTSRB, and CelebA. It also effectively bypasses existing Trojan defenses and human inspection. Our code can be found in https://github.com/RU-System-Software-and-Security/BppAttack.
What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions

We propose a novel one-stage Transformer-based semantic and spatial refined transformer (SSRT) to solve the Human-Object Interaction detection task, which requires to localize humans and objects, and predicts their interactions. Differently from previous Transformer-based HOI approaches, which mostly focus at improving the design of the decoder outputs for the final detection, SSRT introduces two new modules to help select the most relevant object-action pairs within an image and refine the queries' representation using rich semantic and spatial features. These enhancements lead to state-of-the-art results on the two most popular HOI benchmarks: V-COCO and HICO-DET.
OSOP: A Multi-Stage One Shot Object Pose Estimation Framework

We present a novel one-shot method for object detection and 6 DoF pose estimation, that does not require training on target objects. At test time, it takes as input a target image and a textured 3D query model. The core idea is to represent a 3D model with a number of 2D templates rendered from different viewpoints. This enables CNN-based direct dense feature extraction and matching. The object is first localized in 2D, then its approximate viewpoint is estimated, followed by dense 2D-3D correspondence prediction. The final pose is computed with PnP. We evaluate the method on LineMOD, Occlusion, Homebrewed, YCB-V and TLESS datasets and report very competitive performance in comparison to the state-of-the-art methods trained on synthetic data, even though our method is not trained on the object models used for testing.
SoK: Blockchain Light Clients

Blockchain systems, as append-only ledgers, are typically associated with linearly growing participation costs. Therefore, for a blockchain client to interact with the system (query or submit a transaction), it can either pay these costs by downloading, storing and verifying the blockchain history, or forfeit blockchain security guarantees and place its trust on third party intermediary servers. With this problem becoming apparent from early works in the blockchain space, the concept of a light client has been proposed, where a resource-constrained client such as a browser or mobile device can participate in the system by querying and/or submitting transactions without holding the full blockchain but while still inheriting the blockchain’s security guarantees. A plethora of blockchain systems with different light client frameworks and implementations have been proposed, each with different functionalities, assumptions and efficiencies. In this work we provide a systematization of such light client designs. We unify the space by providing a set of definitions on their properties in terms of provided functionality, efficiency and security, and provide future research directions based on our findings.
Unsupervised Homography Estimation with Coplanarity-Aware GAN

Estimating homography from an image pair is a fundamental problem in image alignment. Unsupervised learning methods have received increasing attention in this field due to their promising performance and label-free training. However, existing methods do not explicitly consider the problem of plane-induced parallax, which will make the predicted homography compromised on multiple planes. In this work, we propose a novel method HomoGAN to guide unsupervised homography estimation to focus on the dominant plane. First, a multi-scale transformer network is designed to predict homography from the feature pyramids of input images in a coarse-to-fine fashion. Moreover, we propose an unsupervised GAN to impose coplanarity constraint on the predicted homography, which is realized by using a generator to predict a mask of aligned regions, and then a discriminator to check if two masked feature maps are induced by a single homography. To validate the effectiveness of HomoGAN and its components, we conduct extensive experiments on a large-scale dataset, and results show that our matching error is 22% lower than the previous SOTA method. Code is available at https://github.com/megvii-research/HomoGAN
Volumetric Bundle Adjustment for Online Photorealistic Scene Capture

Efficient photorealistic scene capture is a challenging task. Current online reconstruction systems can operate very efficiently, but images generated from the models captured by these systems are often not photorealistic. Recent approaches based on neural volume rendering can render novel views at high fidelity, but they often require a long time to train, making them impractical for applications that require real-time scene capture. In this paper, we propose a system that can reconstruct photorealistic models of complex scenes in an efficient manner. Our system processes images online, i.e. it can obtain a good quality estimate of both the scene geometry and appearance at roughly the same rate the video is captured. To achieve the efficiency, we propose a hierarchical feature volume using VDB grids. This representation is memory efficient and allows for fast querying of the scene information. Secondly, we introduce a novel optimization technique that improves the efficiency of the bundle adjustment which allows our system to converge to the target camera poses and scene geometry much faster. Experiments on real-world scenes show that our method outperforms existing systems in terms of efficiency and capture quality. To the best of our knowledge, this is the first method that can achieve online photorealistic scene capture.
3DJCG: A Unified Framework for Joint Dense Captioning and Visual Grounding on 3D Point Clouds

Observing that the 3D captioning task and the 3D grounding task contain both shared and complementary information in nature, in this work, we propose a unified framework to jointly solve these two distinct but closely related tasks in a synergistic fashion, which consists of both shared task-agnostic modules and lightweight task-specific modules. On one hand, the shared task-agnostic modules aim to learn precise locations of objects, fine-grained attribute features to characterize different objects, and complex relations between objects, which benefit both captioning and visual grounding. On the other hand, by casting each of the two tasks as the proxy task of another one, the lightweight task-specific modules solve the captioning task and the grounding task respectively. Extensive experiments and ablation study on three 3D vision and language datasets demonstrate that our joint training frame-work achieves significant performance gains for each individual task and finally improves the state-of-the-art performance for both captioning and grounding tasks.
GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature

Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shift since it has seen various styles of images. With the cosine similarity based cost volume as a bridge, the feature will be grafted to an ordinary cost aggregation module. Despite the broad-spectrum representation, such a low-level feature contains much general information which is not aimed at stereo matching. To recover more task-specific information, the grafted feature is further input into a shallow network to be transformed before calculating the cost. Extensive experiments show that the model generalization ability can be improved significantly with this broad-spectrum and task-oriented feature. Specifically, based on two well-known architectures PSMNet and GANet, our methods are superior to other robust algorithms when transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is available at https://github.com/SpadeLiu/Graft-PSMNet.
Towards Efficient and Scalable Sharpness-Aware Minimization

Recently, Sharpness-Aware Minimization (SAM), which connects the geometry of the loss landscape and generalization, has demonstrated a significant performance boost on training large-scale models such as vision transformers. However, the update rule of SAM requires two sequential (non-parallelizable) gradient computations at each step, which can double the computational overhead. In this paper, we propose a novel algorithm LookSAM - that only periodically calculates the inner gradient ascent, to significantly reduce the additional training cost of SAM. The empirical results illustrate that LookSAM achieves similar accuracy gains to SAM while being tremendously faster - it enjoys comparable computational complexity with first-order optimizers such as SGD or Adam. To further evaluate the performance and scalability of LookSAM, we incorporate a layer-wise modification and perform experiments in the large-batch training scenario, which is more prone to converge to sharp local minima. Equipped with the proposed algorithms, we are the first to successfully scale up the batch size when training Vision Transformers (ViTs). With a 64k batch size, we are able to train ViTs from scratch in minutes while maintaining competitive performance. The code is available here: https://github.com/yong-6/LookSAM
HIVE: Evaluating the Human Interpretability of Visual Explanations

As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable. Despite the recent growth of interpretability work, there is a lack of systematic evaluation of proposed techniques. In this work, we introduce HIVE (Human Interpretability of Visual Explanations), a novel human evaluation framework that assesses the utility of explanations to human users in AI-assisted decision making scenarios, and enables falsifiable hypothesis testing, cross-method comparison, and human-centered evaluation of visual interpretability methods. To the best of our knowledge, this is the first work of its kind. Using HIVE, we conduct IRB-approved human studies with nearly 1000 participants and evaluate four methods that represent the diversity of computer vision interpretability works: GradCAM, BagNet, ProtoPNet, and ProtoTree. Our results suggest that explanations engender human trust, even for incorrect predictions, yet are not distinct enough for users to distinguish between correct and incorrect predictions. We open-source HIVE to enable future studies and encourage more human-centered approaches to interpretability research. HIVE can be found at https://princetonvisualai.github.io/HIVE .
Fine-Grained Predicates Learning for Scene Graph Generation

The performance of current Scene Graph Generation models is severely hampered by some hard-to-distinguish predicates, e.g., “woman-on/standing on/walking on-beach” or “woman-near/looking at/in front of-child”. While general SGG models are prone to predict head predicates and existing re-balancing strategies prefer tail categories, none of them can appropriately handle these hard-to-distinguish predicates. To tackle this issue, inspired by fine-grained image classification, which focuses on differentiating among hard-to-distinguish object classes, we propose a method named Fine-Grained Predicates Learning (FGPL) which aims at differentiating among hard-to-distinguish predicates for Scene Graph Generation task. Specifically, we first introduce a Predicate Lattice that helps SGG models to figure out fine-grained predicate pairs. Then, utilizing the Predicate Lattice, we propose a Category Discriminating Loss and an Entity Discriminating Loss, which both contribute to distinguishing fine-grained predicates while maintaining learned discriminatory power over recognizable ones. The proposed model-agnostic strategy significantly boosts the performances of three benchmark models (Transformer, VCTree, and Motif) by 22.8%, 24.1% and 21.7% of Mean Recall (mR@100) on the Predicate Classification sub-task, respectively. Our model also outperforms state-of-the-art methods by a large margin (i.e., 6.1%, 4.6%, and 3.2% of Mean Recall (mR@100)) on the Visual Genome dataset. Codes are publicly available <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/XinyuLyu/FGPL.