GenAI4ELab/papercli-papers huggingface dataset:
Attribution Quality Metrics with Magnitude Alignment
Authors: Chase Walker; Dominic Simon; Kenny Chen; Rickard Ewetz
Venue: IJCAI 2024 (main)

Abstract: Attribution algorithms play an instrumental role in human interpretation of AI models. The methods measure the importance of the input features to the model output decision, which can be displayed as an attribution map for image classifiers. Perturbation tests are the state-of-the-art approach to evaluate the quality of an attribution map. Unfortunately, we observe that perturbation tests fail to consider attribution magnitude, which translates into inconsistent quality scores. In this paper, we propose Magnitude Aligned Scoring (MAS), a new attribution quality metric that measures the alignment between the magnitude of the attributions and the model response. In particular, the metric accounts for both the relative ordering and the magnitude of the pixels within an attribution. In the experimental evaluation, we compare the MAS metric with existing metrics across a wide range of models, datasets, attributions, and evaluations. The results demonstrate that the MAS metric is 4x more sensitive to attribution changes, 2x more consistent, and 1.6x more invariant to baseline modifications. Our code and the referenced appendix are publicly available via https://github.com/chasewalker26/Magnitude-Aligned-Scoring.
CMACE: CMAES-based Counterfactual Explanations for Black-box Models
Authors: Xudong Yin; Yao Yang
Venue: IJCAI 2024 (main)

Abstract: Explanatory Artificial Intelligence plays a vital role in machine learning, due to its widespread application in decision-making scenarios, e.g., credit lending. Counterfactual Explanation (CFE) is a new kind of explanatory method that involves asking “what if ”, i.e. what would have happened if model inputs slightly change. To answer the question, Counterfactual Explanation aims at finding a minimum perturbation in model inputs leading to a different model decision. Compared with model-agnostic approaches, model-specific CFE approaches designed only for specific type of models usually have better performance in finding optimal counterfactual perturbations, owing to access to the inner workings of models. To deal with this dilemma, this work first proposes CMAES-based Counterfactual Explanations (CMACE): an effective model-agnostic counterfactual generating approach based on Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and a warm starting scheme that provides good initialization of the counterfactual's mean and covariance parameters for CMA-ES taking advantage of prior information of training samples. CMACE significantly outperforms another state-of-art (SOTA) model-agnostic approach (Bayesian Counterfactual Generator, BayCon) with various experimental settings. Extensive experiments also demonstrate that CMACE is superior to a SOTA model-specific approach (Flexible Optimizable Counterfactual Explanations for Tree Ensembles, FOCUS) that is designed for tree-based models using gradient-based optimization.
BADFSS: Backdoor Attacks on Federated Self-Supervised Learning
Authors: Jiale Zhang; Chengcheng Zhu; Di Wu; Xiaobing Sun; Jianming Yong; Guodong Long
Venue: IJCAI 2024 (main)

Abstract: Self-supervised learning (SSL) is capable of learning remarkable representations from centrally available data. Recent works further implement federated learning with SSL to learn from rapidly growing decentralized unlabeled images (e.g., from cameras and phones), often resulting from privacy constraints. Extensive attention has been paid to designing new frameworks or methods that achieve better performance for the SSL-based FL. However, such an effort has not yet taken the security of SSL-based FL into consideration. We aim to explore backdoor attacks in the context of SSL-based FL via an in-depth empirical study. In this paper, we propose a novel backdoor attack BADFSS against SSL-based FL. First, BADFSS learns a backdoored encoder via supervised contrastive learning on poison datasets constructed based on local datasets. Then, BADFSS employs attention alignment to enhance the backdoor effect and maintain the consistency between backdoored and global encoders. Moreover, we perform empirical evaluations of the proposed backdoor attacks on four datasets and compared BADFSS with three existing backdoor attacks that are transferred into federated self-supervised learning. The experiments demonstrate that BADFSS outperforms baseline methods and is effective under various settings.
PRASS: Probabilistic Risk-averse Robust Learning with Stochastic Search
Authors: Tianle Zhang; Yanghao Zhang; Ronghui Mu; Jiaxu Liu; Jonathan Fieldsend; Wenjie Ruan
Venue: IJCAI 2024 (main)

Abstract: Deep learning models, despite their remarkable success in various tasks, have been shown to be vulnerable to adversarial perturbations. Although robust learning techniques that consider adversarial risks against worst-case perturbations can effectively increase a model's robustness, they may not always be the most suitable approach. This is due to the fact that in certain scenarios, perturbations are more likely to occur probabilistically rather than being intentionally crafted by attackers. To address this challenge, we propose a novel risk-averse robust learning method based on entropic value-at-risk, called PRASS (Probabilistical Risk-Averse Robust Learning with Stochastic Search). Our approach leverages principles of stochastic optimisation and considers perturbing distributions rather than solely worst-case adversaries. By applying adaptive stochastic search to parameterised distributions, we further enhance the scalability of PRASS to handle distributional robustness. Empirical experiments demonstrate that PRASS outperforms existing state-of-the-art baselines.
A General Black-box Adversarial Attack on Graph-based Fake News Detectors
Authors: Peican Zhu; Zechen Pan; Yang Liu; Jiwei Tian; Keke Tang; Zhen Wang
Venue: IJCAI 2024 (main)

Abstract: Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial attacks that require a specific adjacency matrix. In this paper, we propose the first general black-box adversarial attack framework, i.e., General Attack via Fake Social Interaction (GAFSI), against detectors based on different graph structures. Specifically, as sharing is an important social interaction for GNN-based fake news detectors to construct the graph, we simulate sharing behaviors to fool the detectors. Firstly, we propose a fraudster selection module to select engaged users leveraging local and global information. In addition, a post injection module guides the selected users to create shared relations by sending posts. The sharing records will be added to the social context, leading to a general attack against different detectors. Experimental results on empirical datasets demonstrate the effectiveness of GAFSI.
Bring Metric Functions into Diffusion Models
Authors: Jie An; Zhengyuan Yang; Jianfeng Wang; Linjie Li; Zicheng Liu; Lijuan Wang; Jiebo Luo
Venue: IJCAI 2024 (main)

Abstract: We introduce a Cascaded Diffusion Model (Cas-DM) that improves a Denoising Diffusion Probabilistic Model (DDPM) by effectively incorporating additional metric functions in training. Metric functions such as the LPIPS loss have been proven highly effective in consistency models derived from the score matching. However, for the diffusion counterparts, the methodology and efficacy of adding extra metric functions remain unclear. One major challenge is the mismatch between the noise predicted by a DDPM at each step and the desired clean image that the metric function works well on. To address this problem, we propose Cas-DM, a network architecture that cascades two network modules to effectively apply metric functions to the diffusion model training. The first module, similar to a standard DDPM, learns to predict the added noise and is unaffected by the metric function. The second cascaded module learns to predict the clean image, thereby facilitating the metric function computation. Experiment results show that the proposed diffusion model backbone enables the effective use of the LPIPS loss, improving the image quality (FID, sFID) of diffusion models on various established benchmarks.
Attention Shifting to Pursue Optimal Representation for Adapting Multi-granularity Tasks
Authors: Gairui Bai; Wei Xi; Yihan Zhao; Xinhui Liu; Jizhong Zhao
Venue: IJCAI 2024 (main)

Abstract: Object recognition in open environments, e.g., video surveillance, poses significant challenges due to the inclusion of unknown and multi-granularity tasks (MGT). However, recent methods exhibit limitations as they struggle to capture subtle differences between different parts within an object and adaptively handle MGT. To address this limitation, this paper proposes a Class-semantic Guided Attention Shift (SegAS) method. SegAS transforms adaptive MGT into dynamic combinations of invariant discriminant representations across different levels to effectively enhance adaptability to multi-granularity downstream tasks. Specifically, SegAS incorporates a hardness-based Attention Part Filtering Strategy (ApFS) to dynamically decompose objects into complementary parts based on the object structure and relevance to the instance. Then, SegAS shifts attention to the optimal discriminant region of each part under the guidance of hierarchical class semantics. Finally, a diversity loss is employed to emphasize the importance and distinction of different partial features. Extensive experiments validate SegAS' effectiveness in multi-granularity recognition of three tasks.
SGDCL: Semantic-Guided Dynamic Correlation Learning for Explainable Autonomous Driving
Authors: Chengtai Cao; Xinhong Chen; Jianping Wang; Qun Song; Rui Tan; Yung-Hui Li
Venue: IJCAI 2024 (main)

Abstract: By learning expressive representations, deep learning (DL) has revolutionized autonomous driving (AD). Despite significant advancements, the inherent opacity of DL models engenders public distrust, impeding their widespread adoption. For explainable autonomous driving, current studies primarily concentrate on extracting features from input scenes to predict driving actions and their corresponding explanations. However, these methods underutilize semantics and correlation information within actions and explanations (collectively called categories in this work), leading to suboptimal performance. To address this issue, we propose Semantic-Guided Dynamic Correlation Learning (SGDCL), a novel approach that effectively exploits semantic richness and dynamic interactions intrinsic to categories. SGDCL employs a semantic-guided learning module to obtain category-specific representations and a dynamic correlation learning module to adaptively capture intricate correlations among categories. Additionally, we introduce an innovative loss term to leverage fine-grained co-occurrence statistics of categories for refined regularization. We extensively evaluate SGDCL on two well-established benchmarks, demonstrating its superiority over seven state-of-the-art baselines and a large vision-language model. SGDCL significantly promotes explainable autonomous driving with up to 15.3% performance improvement and interpretable attention scores, bolstering public trust in AD.
MLP-DINO: Category Modeling and Query Graphing with Deep MLP for Object Detection
Authors: Guiping Cao; Wenjian Huang; Xiangyuan Lan; Jianguo Zhang; Dongmei Jiang; Yaowei Wang
Venue: IJCAI 2024 (main)

Abstract: Popular transformer-based detectors detect objects in a one-to-one manner, where both the bounding box and category of each object are predicted only by the single query, leading to the box-sensitive category predictions. Additionally, the initialization of positional queries solely based on the predicted confidence scores or learnable embeddings neglects the significant spatial interrelation between different queries. This oversight leads to an imbalanced spatial distribution of queries (SDQ). In this paper, we propose a new MLP-DINO model to address these issues. Firstly, we present a new Query-Independent Category Supervision (QICS) approach for modeling categories information, decoupling the sensitive bounding box prediction process to improve the detection performance. Additionally, to further improve the category predictions, we introduce a deep MLP model into transformer-based detection framework to capture the long-range and short-range information simultaneously. Thirdly, to balance the SDQ, we design a novel Graph-based Query Selection (GQS) method that distributes each query point in a discrete manner by graphing the spatial information of queries to cover a broader range of potential objects, significantly enhancing the hit-rate of queries. Experimental results on COCO indicate that our MLP-DINO achieves 54.6% AP with only 44M parame ters under 36-epoch setting, greatly outperforming the original DINO by +3.7% AP with fewer parameters and FLOPs. The source codes will be available at https://github.com/Med-Process/MLP-DINO.
CoAtFormer: Vision Transformer with Composite Attention
Authors: Zhiyong Chang; Mingjun Yin; Yan Wang
Venue: IJCAI 2024 (main)

Abstract: Transformer has recently gained significant attention and achieved state-of-the-art performance in various computer vision applications, including image classification, instance segmentation, and object detection. However, the self-attention mechanism underlying the transformer leads to quadratic computational cost with respect to image size,limiting its widespread adoption in state-of-the-art vision backbones. In this paper we introduce an efficient and effective attention module we call Composite Attention. It features parallel branches, enabling the modeling of various global dependencies. In each composite attention module, one branch employs a dynamic channel attention module to capture global channel dependencies, while the other branch utilizes an efficient spatial attention module to extract long-range spatial interactions. In addition, we effectively blending composite attention module with convolutions, and accordingly develop a simple hierarchical vision backbone, dubbed CoAtFormer, by simply repeating the basic building block over multiple stages. Extensive experiments show our CoAtFormer achieves state-of-the-art results on various different tasks. Without any pre-training and extra data, CoAtFormer-Tiny, CoAtFormer-Small, and CoAtFormer-Base achieve 84.4%, 85.3%, and 85.9% top-1 accuracy on ImageNet-1K with 24M, 37M, and 73M parameters, respectively. Furthermore, CoAtFormer also consistently outperform prior work in other vision tasks such as object detection, instance segmentation, and semantic segmentation. When further pretraining on the larger dataset ImageNet-22k, we achieve 88.7% Top-1 accuracy on ImageNet-1K
Enhancing Cross-Modal Retrieval via Visual-Textual Prompt Hashing
Authors: Bingzhi Chen; Zhongqi Wu; Yishu Liu; Biqing Zeng; Guangming Lu; Zheng Zhang
Venue: IJCAI 2024 (main)

Abstract: Cross-modal hashing has garnered considerable research interest due to its rapid retrieval and low storage costs. However, the majority of existing methods suffer from the limitations of context loss and information redundancy, particularly in simulated textual environments enriched with manually annotated tags or virtual descriptions. To mitigate these issues, we propose a novel Visual-Textual Prompt Hashing (VTPH) that aims to bridge the gap between simulated textual and visual modalities within a unified prompt optimization paradigm for cross-modal retrieval. By seamlessly integrating robust reasoning capabilities inherent in large-scale models, we design the visual and textual alignment prompt mechanisms to collaboratively enhance the contextual awareness and semantic capabilities embedded within simulated textual features. Furthermore, an affinity-adaptive contrastive learning strategy is dedicated to dynamically recalibrating the semantic interaction between visual and textual modalities by modeling the nuanced heterogeneity and semantic gaps between simulated and real-world textual environments. To the best of our knowledge, this is the first attempt to integrate both visual and textual prompt learning into cross-modal hashing, facilitating the efficacy of semantic coherence between diverse modalities. Extensive experiments on multiple benchmark datasets consistently demonstrate the superiority and robustness of our VTPH method over state-of-the-art competitors.
Evolutionary Generalized Zero-Shot Learning
Authors: Dubing Chen; Chenyi Jiang; Haofeng Zhang
Venue: IJCAI 2024 (main)

Abstract: Attribute-based Zero-Shot Learning (ZSL) has revolutionized the ability of models to recognize new classes not seen during training. However, with the advancement of large-scale models, the expectations have risen. Beyond merely achieving zero-shot generalization, there is a growing demand for universal models that can continually evolve in expert domains using unlabeled data. To address this, we introduce a scaled-down instantiation of this challenge: Evolutionary Generalized Zero-Shot Learning (EGZSL). This setting allows a low-performing zero-shot model to adapt to the test data stream and evolve online. We elaborate on three challenges of this special task, \ie, catastrophic forgetting, initial prediction bias, and evolutionary data class bias. Moreover, we propose targeted solutions for each challenge, resulting in a generic method capable of continuous evolution from a given initial IGZSL model. Experiments on three popular GZSL benchmark datasets demonstrate that our model can learn from the test data stream while other baselines fail. The codes are available at https://github.com/cdb342/EGZSL.
Cross-Domain Few-Shot Semantic Segmentation via Doubly Matching Transformation
Authors: Jiayi Chen; Rong Quan; Jie Qin
Venue: IJCAI 2024 (main)

Abstract: Cross-Domain Few-shot Semantic Segmentation (CD-FSS) aims to train generalized models that can segment classes from different domains with a few labeled images. Previous works have proven the effectiveness of feature transformation in addressing CD-FSS. However, they completely rely on support images for feature transformation, and repeatedly utilizing a few support images for each class may easily lead to overfitting and overlooking intra-class appearance differences. In this paper, we propose a Doubly Matching Transformation-based Network (DMTNet) to solve the above issue. Instead of completely relying on support images, we propose Self-Matching Transformation (SMT) to construct query-specific transformation matrices based on query images themselves to transform domain-specific query features into domain-agnostic ones. Calculating query-specific transformation matrices can prevent overfitting, especially for the meta-testing stage where only one or several images are used as support images to segment hundreds or thousands of images. After obtaining domain-agnostic features, we exploit a Dual Hypercorrelation Construction (DHC) module to explore the hypercorrelations between the query image with the foreground and background of the support image, based on which foreground and background prediction maps are generated and supervised, respectively, to enhance the segmentation result. In addition, we propose a Test-time Self-Finetuning (TSF) strategy to more accurately self-tune the query prediction in unseen domains. Extensive experiments on four popular datasets show that DMTNet achieves superior performance over state-of-the-art approaches. Code is available at https://github.com/ChenJiayi68/DMTNet.
Bridging LiDAR Gaps: A Multi-LiDARs Domain Adaptation Dataset for 3D Semantic Segmentation
Authors: Shaoyang Chen; Bochun Yang; Yan Xia; Ming Cheng; Siqi Shen; Cheng Wang
Venue: IJCAI 2024 (main)

Abstract: We focus on the domain adaptation problem for 3D semantic segmentation, addressing the challenge of data variability in point clouds collected by different LiDARs. Existing benchmarks often mix different types of datasets, which blurs and complicates segmentation evaluations. Here, we introduce a Multi-LiDARs Domain Adaptation Segmentation (MLDAS) dataset, which contains point-wise semantic annotated point clouds captured simultaneously by a 128-beam LiDAR, a 64-beam LiDAR, a 32-beam LiDAR. We select 31,875 scans from 2 representative scenarios: campus and urban street. Furthermore, we evaluate the current 3D segmentation unsupervised domain adaptation methods on the proposed dataset and propose Hierarchical Segmentation Network with Spatial Consistency (HSSC) as a novel knowledge transfer method to mitigate the domain gap significantly using spatial-temporal consistency constraints. Extensive experiments show that HSSC greatly improves the state-of-the-art cross-domain semantic segmentation methods. Our project is available at https://sychen320.github.io/projects/MLDAS.
A Transformer-Based Adaptive Prototype Matching Network for Few-Shot Semantic Segmentation
Authors: Sihan Chen; Yadang Chen; Yuhui Zheng; Zhi-Xin Yang; Enhua Wu
Venue: IJCAI 2024 (main)

Abstract: Few-shot semantic segmentation (FSS) aims to generate a model for segmenting novel classes using a limited number of annotated samples. Previous FSS methods have shown sensitivity to background noise due to inherent bias, attention bias, and spatial-aware bias. In this study, we propose a Transformer-Based Adaptive Prototype Matching Network to establish robust matching relationships by improving the semantic and spatial perception of query features. The model includes three modules: target enhancement module (TEM), dual constraint aggregation module (DCAM), and dual classification module (DCM). In particular, TEM mitigates inherent bias by exploring the relevance of multi-scale local context to enhance foreground features. Then, DCAM addresses attention bias through the dual semantic-aware attention mechanism to strengthen constraints. Finally, the DCM module decouples the segmentation task into semantic alignment and spatial alignment to alleviate spatial-aware bias. Extensive experiments on PASCAL-5i and COCO-20i confirm the effectiveness of our approach.
D3ETR: Decoder Distillation for Detection Transformer
Authors: Xiaokang Chen; Jiahui Chen; Yan Liu; Jiaxiang Tang; Gang Zeng
Venue: IJCAI 2024 (main)

Abstract: Although various knowledge distillation (KD) methods for CNN-based detectors have been proven effective in improving small students, build- ing baselines and recipes for DETR-based detec- tors remains a challenge. This paper concentrates on the transformer decoder of DETR-based detec- tors and explores KD methods suitable for them. However, the random order of the decoder outputs poses a challenge for knowledge distillation as it provides no direct correspondence between the pre- dictions of the teacher and the student. To this end, we propose MixMatcher that aligns the de- coder outputs of DETR-based teacher and student, by mixing two teacher-student matching strategies for combined advantages. The first strategy, Adap- tive Matching, applies bipartite matching to adap- tively match the outputs of the teacher and the stu- dent in each decoder layer. The second strategy, Fixed Matching, fixes the correspondence between the outputs of the teacher and the student with the same object queries as input, which alleviates in- stability of bipartite matching in Adaptive Match- ing. Using both strategies together produces bet- ter results than using either strategy alone. Based on MixMatcher, we devise Decoder Distillation for DEtection TRansformer (D3ETR), which dis- tills knowledge in decoder predictions and attention maps from the teacher to student. D3ETR shows superior performance on various DETR-based de- tectors with different backbones. For instance, D3ETR improves Conditional DETR-R50-C5 by 8.3 mAP under 12 epochs training setting with Conditional DETR-R101-C5 serving as the teacher. The code will be released.