GenAI4ELab/papercli-papers huggingface dataset:
FORCE: Transferable Visual Jailbreaking Attacks via Feature Over-Reliance CorrEction
Authors: Runqi Lin; Alasdair Paren; Suqin Yuan; Muyang Li; Philip Torr; Adel Bibi; Tongliang Liu
Venue: CVPR 2026 (main)

Abstract: The integration of new modalities enhances the capabilities of multimodal large language models (MLLMs) but also introduces additional vulnerabilities. In particular, simple visual jailbreaking attacks can manipulate open-source MLLMs more readily than sophisticated textual attacks. However, these underdeveloped attacks exhibit extremely limited cross-model transferability, failing to reliably identify vulnerabilities in closed-source MLLMs. In this work, we analyse the loss landscape of these jailbreaking attacks and find that the generated attacks tend to reside in high-sharpness regions, whose effectiveness is highly sensitive to even minor parameter changes during transfer. To further explain the high-sharpness localisations, we analyse their feature representations in both the intermediate layers and the spectral domain, revealing an improper reliance on narrow layer representations and semantically poor frequency components. Building on this, we propose a Feature Over-Reliance CorrEction (FORCE) method, which guides the attack to explore broader feasible regions across layer features and rescales the influence of frequency features according to their semantic content. By eliminating non-generalizable reliance on both layer and spectral features, our method discovers flattened feasible regions for visual jailbreaking attacks, thereby improving cross-model transferability. Extensive experiments demonstrate that our approach effectively facilitates visual red-teaming evaluations against closed-source MLLMs.
Cross-domain Dual-stream Feature Disentanglement for Brain Disorder Prediction with Sparsely Labeled PET
Authors: Huabin Wang; Xinyu Chen; Yuan Zhou; Fei Liu
Venue: CVPR 2026 (main)

Abstract: Positron Emission Tomography (PET) can be used for the early diagnosis of various brain disorders. However, the annotation of PET scans requires the involvement of specialized nuclear medicine experts, making accurately annotated PET data extremely scarce. MRI-based cross-modal domain adaptation methods can improve the brain disorder classification accuracy with sparsely labeled PET data. However, existing methods fail to balance the core requirements of domain discrepancy elimination and modality-specific discriminative information retention in cross-modal tasks. Forced alignment often undermines the core pathological discriminative features of both modalities, making it difficult to meet the collaborative optimization demands of cross-modal brain disorder classification. To address this, we propose a Dual-Stream feature Disentanglement and Alignment (DSDA) framework designed for collaborative optimization of cross-modal domain adaptation and brain disorder classification. This framework first dynamically evaluates and explicitly decouples the critical brain regions relevant to the classification task from the non-critical regions that preserve brain structural integrity. It then applies differential processing to the two types of brain regions: topology-weighted feature alignment for non-critical regions and high-confidence feature fusion for critical regions. This differential processing ensures that the model effectively aligns features while preserving key discriminative information. Extensive experimental results on various datasets (e.g., ADNI, AIBL, and PPMI) demonstrate the effectiveness of DSDA which helps achieve the state-of-the-art performance.
Focal-General Diffusion Model with Semantic Consistent Guidance for Sign Language Production
Authors: Yiheng Yu; Sheng Liu; Yuan Feng; Zhelun Jin; Yining Jiang; Min Xu
Venue: CVPR 2026 (main)

Abstract: Sign Language Production (SLP) aims to translate spoken language into sign sequences, where the main challenge lies in generating coherent and natural poses from discrete glosses (G2P). Existing G2P methods typically treat each pose as an indivisible unit, limiting their ability to capture fine-grained joint-level dependencies and thus degrading pose quality. To address this, we propose the Focal-General Diffusion Model (FGDM), characterized by a pioneering two-stage denoising framework that harmonizes local joint-level dependencies and global coherence. Specifically, in the Focal stage, a novel Adaptive Sign GCN (ASGCN) adaptively models each pose based on contextual correlations, skeletal topology, and semantic conditions, ensuring precise generation of local details. In the General stage, a Transformer-based module refines the entire pose sequence to enhance global coherence and naturalness. Moreover, we introduce a Semantic Consistent Guidance (SCG) mechanism that seamlessly integrates semantic supervision into diffusion training, enforcing tighter alignment between generated pose sequences and their intended gloss semantics. Extensive experiments on PHOENIX14T and USTC-CSL demonstrate that FGDM achieves SOTA performance. Our project page is available at https://yuyiheng-eu.github.io/fgdm/.
MatchMask: Mask-Centric Generative Data Augmentation for Label-Scarce Semantic Segmentation
Authors: Yuqi Lin; Hao Zhang; Wenqi Shao; Shiqu Liu; Zhihong Gu; Wenxiao Wang; Xiaofei He; Kaipeng Zhang
Venue: CVPR 2026 (main)

Abstract: Current semantic segmentation models are very data-hungry and require massive costly pixel-wise human annotations. Generative data augmentation, which scales the train set using generative models, provides a potential remedy. In this paper, we propose MatchMask, a novel mask-centric generative data augmentation approach tailored for label-scarce semantic segmentation. By leveraging a limited set of labeled semantic masks, MatchMask generates diverse, realistic, and well-aligned image-mask pairs, thereby enhancing the performance of semantic segmentation models. Specifically, to adapt existing text-to-image models for semantic image synthesis in the few-shot setting, we first propose a Gradient Probe Method to investigate the role of each layer in the diffusion model. On this basis, a lightweight LoRA-style adapter is designed for critical layers to enable efficient adaptation, coupled with a Layer-adaptive Cross-attention Fusion mechanism. Meanwhile, we present a robust relative filtering principle to suppress incorrectly synthesized regions. Moreover, the proposed approach is extended to MatchMask++ in the semi-supervised setting to take advantage of additional unlabeled data. Experimental results on PASCAL VOC, COCO and ADE20K demonstrate that MatchMask remarkably enhances the performance of segmentation models, surpassing prior data augmentation techniques in various benchmarks, e.g., 67.5%->74.3% mIoU on PASCAL VOC.
E-3DPSM: A State Machine for Event-based Egocentric 3D Human Pose Estimation
Authors: Mayur Deshmukh; Hiroyasu Akada; Helge Rhodin; Christian Theobalt; Vladislav Golyanik
Venue: CVPR 2026 (main)

Abstract: Event cameras offer multiple advantages in monocular egocentric 3D human pose estimation from head-mounted devices, such as millisecond temporal resolution, high dynamic range, and negligible motion blur. Existing methods effectively leverage these properties, but suffer from low 3D estimation accuracy, insufficient in many applications (e.g., immersive VR/AR). This is due to the design not being fully tailored towards event streams (e.g., their asynchronous and continuous nature), leading to high sensitivity to self-occlusions and temporal jitter in the estimates. This paper rethinks the setting and introduces E-3DPSM, an event-driven continuous pose state machine for event-based egocentric 3D human pose estimation. E-3DPSM aligns continuous human motion with fine-grained event dynamics; it evolves latent states and predicts continuous changes in 3D joint positions associated with observed events, which are fused with direct 3D human pose predictions, leading to stable and drift-free final 3D pose reconstructions. E-3DPSM runs in real-time at 80 Hz on a single workstation and sets a new state of the art in experiments on two benchmarks, improving accuracy by up to 19% (MPJPE) and temporal stability by up to 2.7x.
Semi-supervised Echocardiography Video Segmentation via Anchor Semantic Awareness and Continuous Pseudo-label Reforging
Authors: Yunpeng Fang; Yimu Sun; Jingxing Guo; Huisi Wu; Jing Qin
Venue: CVPR 2026 (main)

Abstract: Automatic and accurate echocardiography video segmentation is essential for efficient and repeatable measurements of key clinical functional indicators for the diagnosis of cardiovascular diseases. However, it is an extremely challenging task to obtain high-quality segmentation results throughout the cardiac cycle owing to (1) the inherent speckle noise in echocardiography videos, (2) the complex dynamic motions of cardiac structures, and (3) the scarcity of annotated data. To comprehensively address these challenges, we propose a novel semi-supervised model, EchoForge, which can achieve accurate and real-time echocardiography video segmentation with very limited annotations. EchoForge introduces an Anchor Semantic Awareness (ASA) module that refines ambiguous regions using learnable anchors and propagates structural prototypes across frames to enhance boundary delineation and temporal consistency. Building upon ASA, a Continuous Pseudo-label Reforging (CPR) module progressively integrates and refines pseudo-labels via channel-wise attention, providing robust supervision. Extensive experiments on the CAMUS and EchoNet-Dynamic benchmarks demonstrate that EchoForge outperforms state-of-the-art (SOTA) methods in accuracy while maintaining real-time efficiency. The code is available at https://github.com/YunPeng-Fang/EchoForge.
Memory-Augmented Scene Understanding and Exploration for Open-World Aerial Object-Goal Navigation
Authors: Jiacong Zhou; Jiaxu Miao; Yourun Lin; Xianyun Wang; Jun Xiao; Jun Yu
Venue: CVPR 2026 (main)

Abstract: Aerial object-goal navigation (Aerial ObjectNav) requires an Unmanned Aerial Vehicle (UAV) to navigate to target objects in large-scale outdoor environments using only visual observations and high-level object descriptions, without detailed step-by-step instructions. Existing approaches rely on local observations or short-term history, lacking comprehensive scene understanding and efficient spatial exploration strategies, which constrains their navigation capability in complex aerial scenarios. To address these challenges, we propose OctMem-Agent, an octree memory-augmented framework for aerial object-goal navigation. Specifically, we introduce an Adaptive Octree Memory that incrementally aggregates RGB-D observations into a hierarchical 3D representation, capturing both explored regions and unexplored frontiers across large-scale aerial environments. We further propose a Instruction-Guided Memory Query module that extracts task-relevant scene and exploration tokens through instruction-modulated queries. By integrating these tokens with visual observations and language instructions, OctoMem-Agent achieves comprehensive scene understanding and effective spatial exploration for target localization. Extensive experiments on the Aerial ObjectNav benchmark UAV-ON demonstrate that our method achieves a significant 7.5% improvement in success rate over existing methods, validating the effectiveness of our design.
The Devil is in Attention Sharing: Improving Complex Non-rigid Image Editing Faithfulness via Attention Synergy
Authors: Zhuo Chen; Fanyue Wei; Runze Xu; Jingjing Li; Lixin Duan; Angela Yao; Wen Li
Venue: CVPR 2026 (main)

Abstract: Training-free image editing with large diffusion models has become practical, yet faithfully performing complex non-rigid edits (e.g., pose or shape changes) remains highly challenging. We identify a key underlying cause: attention collapse in existing attention sharing mechanisms, where either positional embeddings or semantic features dominate visual content retrieval, leading to over-editing or under-editing.To address this issue, we introduce SynPS, a method that Synergistically leveragesPositional embeddings and Semantic information for faithful non-rigid image editing. We first propose an editing measurement that quantifies the required editing magnitude at each denoising step. Based on this measurement, we design an attention synergy pipeline that dynamically modulates the influence of positional embeddings, enabling SynPS to balance semantic modifications and fidelity preservation.By adaptively integrating positional and semantic cues, SynPS effectively avoids both over- and under-editing. Extensive experiments on public and newly curated benchmarks demonstrate the superior performance and faithfulness of our approach. Our code will be publicly released.
GTR-Turbo: Merged Checkpoint is Secretly a Free Teacher for Agentic VLM Training
Authors: Tong Wei; Yijun Yang; Changhao Zhang; Junliang Xing; Yuanchun Shi; Zongqing Lu; Deheng Ye
Venue: CVPR 2026 (main)

Abstract: Multi-turn reinforcement learning (RL) for multi-modal agents built upon vision-language models (VLMs) is hampered by sparse rewards and long-horizon credit assignment. Recent methods densify the reward by querying a teacher that provides step-level feedback, e.g., Guided Thought Reinforcement (GTR) and On-Policy Distillation, but rely on costly, often privileged models as the teacher, limiting practicality and reproducibility. We introduce GTR-Turbo, a highly efficient upgrade to GTR that matches its performance without training on or querying an expensive teacher model. Specifically, GTR-Turbo merges the weights of checkpoints produced during ongoing RL training and then uses the resulting merged model as a "free" teacher to guide subsequent RL via supervised fine-tuning or soft logit distillation. This design removes dependence on privileged VLMs (e.g., GPT or Gemini), mitigates the "entropy collapse" observed in prior work, and maintains stable training. Across diverse visual agentic tasks, GTR-Turbo improves the accuracy of the baseline model by 10-30% while reducing wall-clock training time by 50% and compute cost by 60% relative to GTR.
ReMoGen: Real-time Human Interaction-to-Reaction Generation via Modular Learning from Diverse Data
Authors: Yaoqin Ye; Yiteng Xu; Qin Sun; Xinge Zhu; Yujing Sun; Yuexin Ma
Venue: CVPR 2026 (main)

Abstract: Human behaviors in real-world environments are inherently interactive, with an individual's motion shaped by surrounding agents and the scene. Such capabilities are essential for applications in virtual avatars, interactive animation, and human-robot collaboration. We target real-time human interaction-to-reaction generation, which generates the ego's future motion from dynamic multi-source cues, including others' actions, scene geometry, and optional high-level semantic inputs. This task is fundamentally challenging due to (i) limited and fragmented interaction data distributed across heterogeneous single-person, human-human, and human-scene domains, and (ii) the need to produce low-latency yet high-fidelity motion responses during continuous online interaction. To address these challenges, we propose ReMoGen (Reaction Motion Generation), a modular learning framework for real-time interaction-to-reaction generation. ReMoGen leverages a universal motion prior learned from large-scale single-person motion datasets and adapts it to target interaction domains through independently trained Meta-Interaction modules, enabling robust generalization under data-scarce and heterogeneous supervision. To support responsive online interaction, ReMoGen performs segment-level generation together with a lightweight Frame-wise Segment Refinement module that incorporates newly observed cues at the frame level, improving both responsiveness and temporal coherence without expensive full-sequence inference. Extensive experiments across human-human, human-scene, and mixed-modality interaction settings show that ReMoGen produces high-quality, coherent, and responsive reactions, while generalizing effectively across diverse interaction scenarios.
PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
Authors: Yuanzhe Liu; Jingyuan Zhu; Yuchen Mo; Gen Li; Xu Cao; Jin Jin; Yifan Shen; Zhengyuan Li; Tianjiao Yu; Wenzhen Yuan; Fangqiang Ding; Ismini Lourentzou
Venue: CVPR 2026 (main)

Abstract: Recent advancements in vision-language-action (VLA) models have shown promise in robotic manipulation, yet they continue to struggle with long-horizon, multi-step tasks. Existing methods lack internal reasoning mechanisms that can identify task-relevant interaction cues or track progress within a subtask, leading to critical execution errors such as repeated actions, missed steps, and premature termination. To address these challenges, we introduce PALM, a VLA framework that structures policy learning around interaction-centric affordance reasoning and subtask progress cues. PALM distills complementary affordance representations that capture object relevance, contact geometry, spatial placements, and motion dynamics, and serve as task-relevant anchors for visuomotor control. To further stabilize long-horizon execution, PALM predicts continuous within-subtask progress, enabling seamless subtask transitions. Across extensive simulation and real-world experiments, PALM consistently outperforms baselines, achieving a 91.8% success rate on LIBERO-LONG, a 12.5% improvement in average length on CALVIN ABC->D, and a 2ximprovement over real-world baselines across three long-horizon generalization settings.
Decoupling Vision and Language: Codebook Anchored Visual Adaptation
Authors: Jason Wu; Tianchen Zhao; Chang Liu; Jiarui Cai; Zheng Zhang; Zhuowei Li; Aaditya Singh; Xiang Xu; Mani Srivastava; Jonathan Wu
Venue: CVPR 2026 (main)

Abstract: Large Vision-Language Models (LVLMs) use their vision encoders to translate images into representations for downstream reasoning, but the encoders often underperform in domain-specific visual tasks such as medical image diagnosis or fine-grained classification, where representation errors can cascade through the language model, leading to incorrect responses. Existing adaptation methods modify the continuous feature interface between encoder and language model through projector tuning or other parameter-efficient updates, which still couples the two components and requires re-alignment whenever the encoder changes. We introduce CRAFT (Codebook RegulAted Fine-Tuning), a lightweight method that fine-tunes the encoder using a discrete codebook that anchors visual representations to a stable token space, achieving domain adaptation without modifying other parts of the model. This decoupled design allows the adapted encoder to seamlessly boost the performance of LVLMs with different language architectures, as long as they share the same codebook. Empirically, CRAFT achieves an average gain of 13.51% across 10 domain-specific benchmarks such as VQARAD and PlantVillage, while preserving the LLM's linguistic capabilities and outperforming peer methods that operate on continuous tokens.
DSFlash: Comprehensive Panoptic Scene Graph Generation in Realtime
Authors: Julian Lorenz; Vladyslav Kovganko; Elias Kohout; Mrunmai Phatak; Daniel Kienzle; Rainer Lienhart
Venue: CVPR 2026 (main)

Abstract: Scene Graph Generation (SGG) aims to extract a detailed graph structure from an image, a representation that holds significant promise as a robust intermediate step for complex downstream tasks like reasoning for embodied agents. However, practical deployment in real-world applications - especially on resource constrained edge devices - requires speed and resource efficiency, challenges that have received limited attention in existing research. To bridge this gap, we introduce DSFlash, a low-latency model for panoptic scene graph generation designed to overcome these limitations. DSFlash can efficiently process video streams without compromising performance against existing state-of-the-art methods, with smaller DSFlash variants achieving 56 frames per second on a standard RTX 3090 GPU. Crucially, unlike prior approaches that often restrict themselves to salient relationships, DSFlash computes comprehensive scene graphs, offering richer contextual information while maintaining its superior latency. Furthermore, DSFlash is light on resources, requiring less than 24 hours to train on a single, nine-year-old GTX 1080 GPU. This accessibility makes DSFlash particularly well-suited for researchers and practitioners operating with limited computational resources, empowering them to adapt and fine-tune SGG models for specialized applications.
Reliev3R: Relieving Feed-forward 3D Reconstruction from Multi-View Geometric Annotations
Authors: Youyu Chen; Junjun Jiang; Yueru Luo; Kui Jiang; Xianming Liu; Xu Yan; Dave Zhenyu Chen
Venue: CVPR 2026 (main)

Abstract: With recent advances, Feed-forward Reconstruction Models (FFRMs) have demonstrated great potential in reconstruction quality and adaptiveness to multiple downstream tasks. However, the excessive reliance on multi-view geometric annotations, e.g. 3D point maps and camera poses, makes the fully-supervised training scheme of FFRMs difficult to scale up.In this paper, we propose Reliev3R, a weakly-supervised paradigm for training FFRMs from scratch without cost-prohibitive multi-view geometric annotations. Relieving the reliance on geometric sensory data and compute-exhaustive structure-from-motion preprocessing, our method draws 3D knowledge directly from monocular relative depths and image sparse correspondences given by zero-shot predictions of pretrained models.At the core of Reliev3R, we design an ambiguity-aware relative depth loss and a trigonometry-based reprojection loss to facilitate supervision for multi-view geometric consistency.Training from scratch with the less data, Reliev3R catches up with its fully-supervised sibling models, taking a step towards low-cost 3D reconstruction supervisions and scalable FFRMs.
Forging a Dynamic Memory: Retrieval-Guided Continual Learning for Generalist Medical Foundation Models
Authors: Zizhi Chen; Yizhen Gao; Minghao Han; Yizhou Liu; Zhaoyu Chen; Dingkang Yang; Lihua Zhang
Venue: CVPR 2026 (main)

Abstract: Multimodal biomedical Vision-Language Models (VLMs) exhibit immense potential in the field of Continual Learning (CL). However, they confront a core dilemma: how to preserve fine-grained intra-modality features while bridging the significant domain gap across different modalities. To address this challenge, we propose a comprehensive framework. Leveraging our 18-million multimodal and comprehensive medical retrieval database derived from PubMed scientific papers, we pioneer the integration of Retrieval-Augmented Generation (RAG) into CL. Specifically, we employ a multi-modal, multi-layer RAG system that provides real-time guidance for model fine-tuning through dynamic, on-demand knowledge retrieval. Building upon this, we introduce a dynamic knowledge distillation framework. This framework precisely resolves the aforementioned core dilemma by dynamically modulating the importance of the parameter space, the granularity of the distilled knowledge, and the data distribution of the reference dataset in accordance with the required level of detail. To thoroughly validate the clinical value of our strategy, we have designed a more rigorous Medical Generalist Task Incremental Learning (MGTIL) benchmark. This benchmark is engineered to simultaneously evaluate the model's capacity for adaptation to significant domain shifts, retention of subtle intra-domain features, and real-time learning of novel and complex medical tasks. Our method achieves stable SOTA results across all metrics, with https://github.com/CZZZZZZZZZZZZZZZZZ/PRIMED code available.