pwc-archive/papers-with-abstracts huggingface dataset:
SAM2-LOVE: Segment Anything Model 2 in Language-aided Audio-Visual Scenes
Authors: Yuji Wang, Haoran Xu, Yong liu, Jiaze Li, Yansong Tang
Date: 2025-06-02 00:00:00
Proceeding: CVPR 2025 1
Tasks: Scene Understanding

Reference Audio-Visual Segmentation (Ref-AVS) aims to provide a pixel-wise scene understanding in Language-aided Audio-Visual Scenes (LAVS). This task requires the model to continuously segment objects referred to by text and audio from a video. Previous dual-modality methods always fail due to the lack of a third modality and the existing triple-modality method struggles with spatio-temporal consistency, leading to the target shift of different frames. In this work, we introduce a novel framework, termed SAM2-LOVE, which integrates textual, audio, and visual representations into a learnable token to prompt and align SAM2 for achieving Ref-AVS in the LAVS. Technically, our approach includes a multimodal fusion module aimed at improving multimodal understanding of SAM2, as well as token propagation and accumulation strategies designed to enhance spatio-temporal consistency without forgetting historical information. We conducted extensive experiments to demonstrate that SAM2-LOVE outperforms the SOTA by 8.5\% in $\mathcal{J\&F}$ on the Ref-AVS benchmark and showcase the simplicity and effectiveness of the components. Our code will be available here.
SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost
Authors: Haiyang Mei, Pengyu Zhang, Mike Zheng Shou
Date: 2025-06-02 00:00:00
Proceeding: CVPR 2025 1
Tasks: Image Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

Foundation models like the Segment Anything Model (SAM) have significantly advanced promptable image segmentation in computer vision. However, extending these capabilities to videos presents substantial challenges, particularly in ensuring precise and temporally consistent mask propagation in dynamic scenes. SAM 2 attempts to address this by training a model on massive image and video data from scratch to learn complex spatiotemporal associations, resulting in huge training costs that hinder research and practical deployment. In this paper, we introduce SAM-I2V, an effective image-to-video upgradation method for cultivating a promptable video segmentation (PVS) model. Our approach strategically upgrades the pre-trained SAM to support PVS, significantly reducing training complexity and resource requirements. To achieve this, we introduce three key innovations: (i) an image-to-video feature extraction upgrader built upon SAM's static image encoder to enable spatiotemporal video perception, (ii) a memory filtering strategy that selects the most relevant past frames for more effective utilization of historical information, and (iii) a memory-as-prompt mechanism leveraging object memory to ensure temporally consistent mask propagation in dynamic scenes. Comprehensive experiments demonstrate that our method achieves over 90% of SAM 2's performance while using only 0.2% of its training cost. Our work presents a resource-efficient pathway to PVS, lowering barriers for further research in PVS model design and enabling broader applications and advancements in the field. Code and model are available at: https://github.com/showlab/SAM-I2V.
AuralSAM2: Enabling SAM2 Hear Through Pyramid Audio-Visual Feature Prompting
Authors: Yuyuan Liu, Yuanhong Chen, Chong Wang, Junlin Han, Junde Wu, Can Peng, Jingkun Chen, Yu Tian, Gustavo Carneiro
Date: 2025-06-01 00:00:00
Tasks: Contrastive Learning, Decoder, Prompt Engineering

Segment Anything Model 2 (SAM2) exhibits strong generalisation for promptable segmentation in video clips; however, its integration with the audio modality remains underexplored. Existing approaches mainly follow two directions: (1) injecting adapters into the image encoder to receive audio signals, which incurs efficiency costs during prompt engineering, and (2) leveraging additional foundation models to generate visual prompts for the sounding objects, which are often imprecisely localised, leading to misguidance in SAM2. Moreover, these methods overlook the rich semantic interplay between hierarchical visual features and other modalities, resulting in suboptimal cross-modal fusion. In this work, we propose AuralSAM2, comprising the novel AuralFuser module, which externally attaches to SAM2 to integrate features from different modalities and generate feature-level prompts, guiding SAM2's decoder in segmenting sounding targets. Such integration is facilitated by a feature pyramid, further refining semantic understanding and enhancing object awareness in multimodal scenarios. Additionally, the audio-guided contrastive learning is introduced to explicitly align audio and visual representations and to also mitigate biases caused by dominant visual patterns. Results on public benchmarks show that our approach achieves remarkable improvements over the previous methods in the field. Code is available at https://github.com/yyliu01/AuralSAM2.
Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light
Authors: Ali Hassani, Fengzhe Zhou, Aditya Kane, Jiannan Huang, Chieh-Yun Chen, Min Shi, Steven Walton, Markus Hoehnerbach, Vijay Thakkar, Michael Isaev, Qinsheng Zhang, Bing Xu, Haicheng Wu, Wen-mei Hwu, Ming-Yu Liu, Humphrey Shi
Date: 2025-04-23 00:00:00

Many sparse attention mechanisms such as Neighborhood Attention have typically failed to consistently deliver speedup over the self attention baseline. This is largely due to the level of complexity in attention infrastructure, and the rapid evolution of AI hardware architecture. At the same time, many state-of-the-art foundational models, particularly in computer vision, are heavily bound by attention, and need reliable sparsity to escape the O(n^2) complexity. In this paper, we study a class of promising sparse attention mechanisms that focus on locality, and aim to develop a better analytical model of their performance improvements. We first introduce Generalized Neighborhood Attention (GNA), which can describe sliding window, strided sliding window, and blocked attention. We then consider possible design choices in implementing these approaches, and create a simulator that can provide much more realistic speedup upper bounds for any given setting. Finally, we implement GNA on top of a state-of-the-art fused multi-headed attention (FMHA) kernel designed for the NVIDIA Blackwell architecture in CUTLASS. Our implementation can fully realize the maximum speedup theoretically possible in many perfectly block-sparse cases, and achieves an effective utilization of 1.3 petaFLOPs/second in FP16. In addition, we plug various GNA configurations into off-the-shelf generative models, such as Cosmos-7B, HunyuanVideo, and FLUX, and show that it can deliver 28% to 46% end-to-end speedup on B200 without any fine-tuning. We will open source our simulator and Blackwell kernels directly through the NATTEN project.
OD3: Optimization-free Dataset Distillation for Object Detection
Authors: Salwa K. Al Khatib, Ahmed Elhagry, Shitong Shao, Zhiqiang Shen
Date: 2025-06-02 00:00:00
Tasks: Dataset Distillation, image-classification, Image Classification, object-detection, Object Detection

Training large neural networks on large-scale datasets requires substantial computational resources, particularly for dense prediction tasks such as object detection. Although dataset distillation (DD) has been proposed to alleviate these demands by synthesizing compact datasets from larger ones, most existing work focuses solely on image classification, leaving the more complex detection setting largely unexplored. In this paper, we introduce OD3, a novel optimization-free data distillation framework specifically designed for object detection. Our approach involves two stages: first, a candidate selection process in which object instances are iteratively placed in synthesized images based on their suitable locations, and second, a candidate screening process using a pre-trained observer model to remove low-confidence objects. We perform our data synthesis framework on MS COCO and PASCAL VOC, two popular detection datasets, with compression ratios ranging from 0.25% to 5%. Compared to the prior solely existing dataset distillation method on detection and conventional core set selection methods, OD3 delivers superior accuracy, establishes new state-of-the-art results, surpassing prior best method by more than 14% on COCO mAP50 at a compression ratio of 1.0%. Code and condensed datasets are available at: https://github.com/VILA-Lab/OD3.
Hyperbolic Category Discovery
Authors: Yuanpei Liu, Zhenqi He, Kai Han
Date: 2025-04-08 00:00:00
Proceeding: CVPR 2025 1
Tasks: Transfer Learning

Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention. Given a dataset that includes both labelled and unlabelled images, GCD aims to categorize all images in the unlabelled subset, regardless of whether they belong to known or unknown classes. In GCD, the common practice typically involves applying a spherical projection operator at the end of the self-supervised pretrained backbone, operating within Euclidean or spherical space. However, both of these spaces have been shown to be suboptimal for encoding samples that possesses hierarchical structures. In contrast, hyperbolic space exhibits exponential volume growth relative to radius, making it inherently strong at capturing the hierarchical structure of samples from both seen and unseen categories. Therefore, we propose to tackle the category discovery challenge in the hyperbolic space. We introduce HypCD, a simple \underline{Hyp}erbolic framework for learning hierarchy-aware representations and classifiers for generalized \underline{C}ategory \underline{D}iscovery. HypCD first transforms the Euclidean embedding space of the backbone network into hyperbolic space, facilitating subsequent representation and classification learning by considering both hyperbolic distance and the angle between samples. This approach is particularly helpful for knowledge transfer from known to unknown categories in GCD. We thoroughly evaluate HypCD on public GCD benchmarks, by applying it to various baseline and state-of-the-art methods, consistently achieving significant improvements.
DiffCAP: Diffusion-based Cumulative Adversarial Purification for Vision Language Models
Authors: Jia Fu, Yongtao Wu, Yihang Chen, Kunyu Peng, Xiao Zhang, Volkan Cevher, Sepideh Pashami, Anders Holst
Date: 2025-06-04 00:00:00
Tasks: Adversarial Purification, Denoising

Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to perturbations poses a significant threat to their reliability in real-world applications. Despite often being imperceptible to humans, these perturbations can drastically alter model outputs, leading to erroneous interpretations and decisions. This paper introduces DiffCAP, a novel diffusion-based purification strategy that can effectively neutralize adversarial corruptions in VLMs. We observe that adding minimal noise to an adversarially corrupted image significantly alters its latent embedding with respect to VLMs. Building on this insight, DiffCAP cumulatively injects random Gaussian noise into adversarially perturbed input data. This process continues until the embeddings of two consecutive noisy images reach a predefined similarity threshold, indicating a potential approach to neutralize the adversarial effect. Subsequently, a pretrained diffusion model is employed to denoise the stabilized image, recovering a clean representation suitable for the VLMs to produce an output. Through extensive experiments across six datasets with three VLMs under varying attack strengths in three task scenarios, we show that DiffCAP consistently outperforms existing defense techniques by a substantial margin. Notably, DiffCAP significantly reduces both hyperparameter tuning complexity and the required diffusion time, thereby accelerating the denoising process. Equipped with strong theoretical and empirical support, DiffCAP provides a robust and practical solution for securely deploying VLMs in adversarial environments.
PRJ: Perception-Retrieval-Judgement for Generated Images
Authors: Qiang Fu, Zonglei Jing, Zonghao Ying, Xiaoqian Li
Date: 2025-06-04 00:00:00
Tasks: Descriptive, Retrieval

The rapid progress of generative AI has enabled remarkable creative capabilities, yet it also raises urgent concerns regarding the safety of AI-generated visual content in real-world applications such as content moderation, platform governance, and digital media regulation. This includes unsafe material such as sexually explicit images, violent scenes, hate symbols, propaganda, and unauthorized imitations of copyrighted artworks. Existing image safety systems often rely on rigid category filters and produce binary outputs, lacking the capacity to interpret context or reason about nuanced, adversarially induced forms of harm. In addition, standard evaluation metrics (e.g., attack success rate) fail to capture the semantic severity and dynamic progression of toxicity. To address these limitations, we propose Perception-Retrieval-Judgement (PRJ), a cognitively inspired framework that models toxicity detection as a structured reasoning process. PRJ follows a three-stage design: it first transforms an image into descriptive language (perception), then retrieves external knowledge related to harm categories and traits (retrieval), and finally evaluates toxicity based on legal or normative rules (judgement). This language-centric structure enables the system to detect both explicit and implicit harms with improved interpretability and categorical granularity. In addition, we introduce a dynamic scoring mechanism based on a contextual toxicity risk matrix to quantify harmfulness across different semantic dimensions. Experiments show that PRJ surpasses existing safety checkers in detection accuracy and robustness while uniquely supporting structured category-level toxicity interpretation.
Robustness in Both Domains: CLIP Needs a Robust Text Encoder
Authors: Elias Abad Rocamora, Christian Schlarmann, Naman Deep Singh, Yongtao Wu, Matthias Hein, Volkan Cevher
Date: 2025-06-03 00:00:00

Adversarial input attacks can cause a significant shift of CLIP embeddings. This can affect the downstream robustness of models incorporating CLIP in the pipeline, such as text-to-image generative models or large vision language models. While some efforts have been done towards making the CLIP image encoders robust, the robustness of text encoders remains unexplored. In this work, we cover this gap in the literature. We propose LEAF: an efficient adversarial finetuning method for the text domain, with the ability to scale to large CLIP models. Our models significantly improve the zero-shot adversarial accuracy in the text domain, while maintaining the vision performance provided by robust image encoders. When combined with text-to-image diffusion models, we can improve the generation quality under adversarial noise. When employing our robust CLIP encoders in multimodal retrieval tasks, we improve the recall under adversarial noise over standard CLIP models. Finally, we show that robust text encoders facilitate better reconstruction of input text from its embedding via direct optimization.
On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
Authors: Ning Zhang, Henry Kenlay, Li Zhang, Mihai Cucuringu, Xiaowen Dong
Date: 2025-06-01 00:00:00

Graph convolutional neural networks (GCNNs) have emerged as powerful tools for analyzing graph-structured data, achieving remarkable success across diverse applications. However, the theoretical understanding of the stability of these models, i.e., their sensitivity to small changes in the graph structure, remains in rather limited settings, hampering the development and deployment of robust and trustworthy models in practice. To fill this gap, we study how perturbations in the graph topology affect GCNN outputs and propose a novel formulation for analyzing model stability. Unlike prior studies that focus only on worst-case perturbations, our distribution-aware formulation characterizes output perturbations across a broad range of input data. This way, our framework enables, for the first time, a probabilistic perspective on the interplay between the statistical properties of the node data and perturbations in the graph topology. We conduct extensive experiments to validate our theoretical findings and demonstrate their benefits over existing baselines, in terms of both representation stability and adversarial attacks on downstream tasks. Our results demonstrate the practical significance of the proposed formulation and highlight the importance of incorporating data distribution into stability analysis.
Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
Authors: Bogdan Chornomaz, Yonatan Koren, Shay Moran, Tom Waknine
Date: 2025-06-03 00:00:00

We study the problem of learning in the presence of an adversary that can corrupt an $\eta$ fraction of the training examples with the goal of causing failure on a specific test point. In the realizable setting, prior work established that the optimal error under such instance-targeted poisoning attacks scales as $\Theta(d\eta)$, where $d$ is the VC dimension of the hypothesis class arXiv:2210.02713. In this work, we resolve the corresponding question in the agnostic setting. We show that the optimal excess error is $\tilde{\Theta}(\sqrt{d\eta})$, answering one of the main open problems left by Hanneke et al. To achieve this rate, it is necessary to use randomized learners: Hanneke et al. showed that deterministic learners can be forced to suffer error close to 1, even under small amounts of poisoning. Perhaps surprisingly, our upper bound remains valid even when the learner's random bits are fully visible to the adversary . In the other direction, our lower bound is stronger than standard PAC-style bounds: instead of tailoring a hard distribution separately for each sample size, we exhibit a single fixed distribution under which the adversary can enforce an excess error of $\Omega(\sqrt{d\eta})$ infinitely often.
On the Robustness of Tabular Foundation Models: Test-Time Attacks and In-Context Defenses
Authors: Mohamed Djilani, Thibault Simonetto, Karim TIT, Florian Tambon, Paul Récamier, Salah Ghamizi, Maxime Cordy, Mike Papadakis
Date: 2025-06-03 00:00:00
Tasks: In-Context Learning

Recent tabular Foundational Models (FM) such as TabPFN and TabICL, leverage in-context learning to achieve strong performance without gradient updates or fine-tuning. However, their robustness to adversarial manipulation remains largely unexplored. In this work, we present a comprehensive study of the adversarial vulnerabilities of tabular FM, focusing on both their fragility to targeted test-time attacks and their potential misuse as adversarial tools. We show on three benchmarks in finance, cybersecurity and healthcare, that small, structured perturbations to test inputs can significantly degrade prediction accuracy, even when training context remain fixed. Additionally, we demonstrate that tabular FM can be repurposed to generate transferable evasion to conventional models such as random forests and XGBoost, and on a lesser extent to deep tabular models. To improve tabular FM, we formulate the robustification problem as an optimization of the weights (adversarial fine-tuning), or the context (adversarial in-context learning). We introduce an in-context adversarial training strategy that incrementally replaces the context with adversarial perturbed instances, without updating model weights. Our approach improves robustness across multiple tabular benchmarks. Together, these findings position tabular FM as both a target and a source of adversarial threats, highlighting the urgent need for robust training and evaluation practices in this emerging paradigm.
ReconXF: Graph Reconstruction Attack via Public Feature Explanations on Privatized Node Features and Labels
Authors: Rishi Raj Sahoo, Rucha Bhalchandra Joshi, Subhankar Mishra
Date: 2025-06-02 00:00:00
Tasks: Denoising, Graph Reconstruction, Reconstruction Attack

Graph Neural Networks (GNNs) achieve high performance across many applications but function as black-box models, limiting their use in critical domains like healthcare and criminal justice. Explainability methods address this by providing feature-level explanations that identify important node attributes for predictions. These explanations create privacy risks. Combined with auxiliary information, feature explanations can enable adversaries to reconstruct graph structure, exposing sensitive relationships. Existing graph reconstruction attacks assume access to original auxiliary data, but practical systems use differential privacy to protect node features and labels while providing explanations for transparency. We study a threat model where adversaries access public feature explanations along with privatized node features and labels. We show that existing explanation-based attacks like GSEF perform poorly with privatized data due to noise from differential privacy mechanisms. We propose ReconXF, a graph reconstruction attack for scenarios with public explanations and privatized auxiliary data. Our method adapts explanation-based frameworks by incorporating denoising mechanisms that handle differential privacy noise while exploiting structural signals in explanations. Experiments across multiple datasets show ReconXF outperforms SoTA methods in privatized settings, with improvements in AUC and average precision. Results indicate that public explanations combined with denoising enable graph structure recovery even under the privacy protection of auxiliary data. Code is available at (link to be made public after acceptance).
Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks
Authors: Artun Saday, Yaşar Cahit Yıldırım, Cem Tekin
Date: 2025-06-02 00:00:00

We address the problem of Gaussian Process (GP) optimization in the presence of unknown and potentially varying adversarial perturbations. Unlike traditional robust optimization approaches that focus on maximizing performance under worst-case scenarios, we consider a robust satisficing objective, where the goal is to consistently achieve a predefined performance threshold $\tau$, even under adversarial conditions. We propose two novel algorithms based on distinct formulations of robust satisficing, and show that they are instances of a general robust satisficing framework. Further, each algorithm offers different guarantees depending on the nature of the adversary. Specifically, we derive two regret bounds: one that is sublinear over time, assuming certain conditions on the adversary and the satisficing threshold $\tau$, and another that scales with the perturbation magnitude but requires no assumptions on the adversary. Through extensive experiments, we demonstrate that our approach outperforms the established robust optimization methods in achieving the satisficing objective, particularly when the ambiguity set of the robust optimization framework is inaccurately specified.