GenAI4ELab/papercli-papers huggingface dataset:
CLEX: Complementary Label Exchange Learning for Noisy Facial Expression Recognition
Authors: Lin Wang; Fang Liu; Xiaofen Xing; Kailing Guo; Xiangmin Xu
Venue: CVPR 2026 (main)

Abstract: Facial expression recognition (FER) in the wild is severely hampered by label noise and annotation ambiguity. Existing methods, including sample selection, label ensembling, and consistency regularization, primarily rely on ordinary label supervision and offer limited control over non-target predictions, leading to spurious activations and overfitting to noisy labels. To address this limitation, we propose a novel learning framework, named Complementary Label Exchange Learning (CLEX), to enhance robustness by exchanging knowledge from non-target predictions across augmented views. Specifically, CLEX comprises three synergistic components. First, Stochastic Non-Target Logit Exchange randomly swaps a subset of non-target logits between original and augmented views to couple error-prone predictions, creating robust consistency constraints. Second, Scale-Invariant Logit Normalization eliminates magnitude artifacts through Lp-norm normalization, ensuring that regularization operates over geometrically meaningful directions rather than being dominated by arbitrary scales. Third, Complementary Suppression Loss selectively penalizes spurious activations over a randomly retained subset of non-target classes, avoiding the uniform shrinkage that hampers discriminative learning. To further stabilize training, we incorporate attention consistency regularization that enforces spatial alignment between augmented views, while retaining auxiliary cross-entropy to preserve semantic localization capability. Extensive experiments across multiple benchmark FER datasets (RAF-DB, FERPlus, and AffectNet) demonstrate that CLEX consistently outperforms existing robust FER learning approaches.
OneHOI: Unifying Human-Object Interaction Generation and Editing
Authors: Jiun Tian Hoe; Weipeng Hu; Xudong Jiang; Yap-Peng Tan; Chee Seng Chan
Venue: CVPR 2026 (main)

Abstract: Human-Object Interaction (HOI) modelling captures how humans act upon and relate to objects, typically expressed as <person, action, object> triplets. Existing approaches split into two disjoint families: HOI generation synthesises scenes from structured triplets and layout, but fails to integrate mixed conditions like HOI and object-only entities; and HOI editing modifies interactions via text, yet struggles to decouple pose from physical contact and scale to multiple interactions. We introduce OneHOI, a unified diffusion transformer framework that consolidates HOI generation and editing into a single conditional denoising process driven by shared structured interaction representations. At its core, the Relational Diffusion Transformer (R-DiT) models verb-mediated relations through role- and instance-aware HOI tokens, layout-based spatial Action Grounding, a Structured HOI Attention to enforce interaction topology, and HOI RoPE to disentangle multi-HOI scenes. Trained jointly with modality dropout on our HOI-Edit-44K, along with HOI and object-centric datasets, OneHOI supports layout-guided, layout-free, arbitrary-mask, and mixed-condition control, achieving state-of-the-art results across both HOI generation and editing.
Reevaluating the Intra-Modal Misalignment Hypothesis in CLIP
Authors: Jonas Herzog; Yue Wang
Venue: CVPR 2026 (main)

Abstract: Recent research suggested that the embeddings produced by CLIP-like contrastive language-image training are suboptimal for image-only tasks. The main theory is that the inter-modal (language-image) alignment loss ignores intra-modal (image-image) alignment, leading to poorly calibrated distances between images. In this study, we question this intra-modal misalignment hypothesis. We reexamine its foundational theoretical argument, the indicators used to support it, and the performance metrics affected. For the theoretical argument, we demonstrate that there are no such supposed degrees of freedom for image embedding distances. For the empirical measures, our findings reveal they yield similar results for language-image trained models (CLIP, SigLIP) and image-image trained models (DINO, SigLIP2). This indicates the observed phenomena do not stem from a misalignment specific to the former. Experiments on the commonly studied intra-modal tasks retrieval and few-shot classification confirm that addressing supposed misalignment is unnecessary for best performance.
Rel-Zero: Harnessing Patch-Pair Invariance for Robust Zero-Watermarking Against AI Editing
Authors: Pengzhen Chen; Yanwei Liu; Xiaoyan Gu; Xiaojun Chen; Wu Liu; Weiping Wang
Venue: CVPR 2026 (main)

Abstract: Recent advancements in diffusion-based image editing pose a significant threat to the authenticity of digital visual content. Traditional embedding-based watermarking methods often introduce perceptible perturbations to maintain robustness, inevitably compromising visual fidelity. Meanwhile, existing zero-watermarking approaches, typically relying on global image features, struggle to withstand sophisticated manipulations. In this work, we uncover a key observation: while individual image patches undergo substantial alterations during AI-based editing, the relational distance between patch pairs remains relatively invariant. Leveraging this property, we propose Relational Zero-Watermarking (Rel-Zero), a novel framework that requires no modification to the original image but derives a unique zero-watermark from these editing-invariant patch relations. By grounding the watermark in intrinsic structural consistency rather than absolute appearance, Rel-Zero provides a non-invasive yet resilient mechanism for content authentication. Extensive experiments demonstrate that Rel-Zero achieves substantially improved robustness across diverse editing models and manipulations compared to prior zero-watermarking approaches.
Joint Spectral Image Reconstruction and Semantic Segmentation with Cooperative Unfolding
Authors: Zijun He; Ping Wang; Xiaodong Wang; Chang Chen; Xin Yuan
Venue: CVPR 2026 (main)

Abstract: Coded Aperture Snapshot Spectral Imaging (CASSI) is an emerging hyperspectral image (HSI) acquisition technique for downstream semantic segmentation. Due to the ill-posedness nature of CASSI systems, typical solutions are compelled to conduct a two-stage reconstruction-then-segmentation pipeline, namely viewing them as two separate tasks. However, we observe that such two tasks are interrelated and mutually reinforcing for representation learning, and thus separating them limits the overall accuracy and efficiency. To this end, we propose the first Cooperative Reconstruction-Segmentation Deep Unfolding Network (CRSDUN) to solve the reconstruction and segmentation tasks in parallel. To make the two mutually reinforcing, we introduce the Cross-Aggregated Super-Token Attention (CASTA) mechanism to enhance the representation interactions between HSI reconstruction and semantic segmentation. Extensive experiments on both synthetic and real-world HSI reconstruction-segmentation datasets demonstrate that our method achieves state-of-the-art in both spectral reconstruction and semantic segmentation. The code is available at \href https://github.com/zjhe02/CRSDUN  https://github.com/zjhe02/CRSDUN .
NOWA: Null-space Optical Watermark for Invisible Capture Fingerprinting and Tamper Localization
Authors: Edwin Vargas; Jhon Lopez; Henry Arguello; Ashok Veeraraghavan
Venue: CVPR 2026 (main)

Abstract: Ensuring the authenticity and ownership of digital images is increasingly challenging as modern editing tools enable highly realistic forgeries. Existing image protection systems mainly rely on digital watermarking, which is susceptible to sophisticated digital attacks. To address this limitation, we propose a hybrid optical-digital framework that incorporates physical authentication cues during image formation and preserves them through a learned reconstruction process. At the optical level, a phase mask in the camera aperture produces a Null-space Optical Watermark (NOWA) that lies in the Null Space of the imaging operator and therefore remains invisible in the captured image. Then, a Null-Space Network (NSN) performs measurement-consistent reconstruction that delivers high-quality protected images while preserving the NOWA signature. The proposed design enables tamper localization by projecting the image onto the camera's null space and detecting pixel-level inconsistencies. Our design preserves perceptual quality, resists common degradations such as compression, and establishes a structural security asymmetry: without access to the optical or NSN parameters, adversaries cannot forge the NOWA signature. Experiments with simulations and a prototype camera demonstrate competitive performance in terms of image quality preservation and tamper localization accuracy compared to state-of-the-art digital watermarking and learning-based authentication methods.
Representation-Steered Incremental Adapter-Tuning for Class-Incremental Learning with Pre-Trained Models
Authors: Jiarui Zhao; Libo Huang; Xiangqi Li; Zhulin An; Chuanguang Yang; Yu Wang; Boyu Diao; Yongjun Xu
Venue: CVPR 2026 (main)

Abstract: Class-Incremental Learning (CIL) aims to develop models to continuously learn new classes without forgetting learned old ones. Recent advances combine pre-trained models with parameter-efficient fine-tuning, achieving promising results. However, these approaches typically allocate new trainable parameters for each task, causing the model size to grow linearly with task number. Moreover, they lack explicit mechanisms to structure a coherent and discriminative representation space across tasks. To address these limitations, we propose Representation-Steered Incremental Adapter Tuning (RSIAT). RSIAT maintains a single shared adapter for all tasks, eliminating parameter growth during incremental learning. In the base task, we introduce a representation-steering loss that enhances discriminative feature learning while facilitating future task adaptation. During incremental tasks, a residual autoencoder-based projector aligns feature distributions between old and new tasks, preserving representation consistency without over-constraining the shared adapter. Extensive experiments on six CIL benchmarks demonstrate that RSIAT significantly outperforms state-of-the-art methods in both performance and parameter efficiency, achieving superior stability-plasticity trade-offs with minimal trainable parameters. Code is available at https://github.com/zjrzjrz/RSIAT.
HOG-Layout: Hierarchical 3D Scene Generation, Optimization and Editing via Vision-Language Models
Authors: Haiyan Jiang; Deyu Zhang; Dongdong Weng; Weitao Song; Henry Been-Lirn Duh
Venue: CVPR 2026 (main)

Abstract: 3D layout generation and editing play a crucial role in Embodied AI and immersive VR interaction. However, manual creation requires tedious labor, while data-driven generation often lacks diversity. The emergence of large models introduces new possibilities for 3D scene synthesis. We present HOG-Layout that enables text-driven hierarchical scene generation, optimization and real-time scene editing with large language models (LLMs) and vision-language models (VLMs). HOG-Layout improves scene semantic consistency and plausibility through retrieval-augmented generation (RAG) technology, incorporates an optimization module to enhance physical consistency, and adopts a hierarchical representation to enhance inference and optimization, achieving real-time editing. Experimental results demonstrate that HOG-Layout produces more reasonable environments compared with existing baselines, while supporting fast and intuitive scene editing.
SeD-UD: An Influence-Driven and Hierarchically-Decoupled Information Bottleneck for Multimodal Intent Recognition
Authors: Qin Li; Wenbo Zhang; Limei Liu; Han Peng; Junfeng Yang; Guanying Xu
Venue: CVPR 2026 (main)

Abstract: Multimodal intent recognition (MIR) is hindered by substantial redundancy and noise originating from text, speech, and visual inputs, which weakens feature distinctiveness and ultimately harms recognition performance. Although recent approaches based on the information bottleneck (IB) principle mitigate this issue via feature compression and reconstruction to obtain compact and noise-reduced representations, they still encounter two major drawbacks. First, conventional IB employs a fixed bottleneck dimension, making it unable to accommodate sample-dependent variations in redundancy and noise. Second, simultaneously handling redundancy and noise within a single compression process leads to incomplete feature purification. In this paper, we propose a novel framework named SeD-UD, which incorporates influence-driven input-adaptive bottleneck (IDAB) modules following a hierarchically-decoupled IB structure. Given a redundancy/noise influence factor, IDAB dynamically adjusts dimensions and selects the optimal parameters for compression and reconstruction, thereby achieving the best trade-off between information preservation and interference suppression. The IB structure performs hierarchically-decoupled processing of redundancy and noise via separated de-redundancy and unified denoising based on IDAB modules. Extensive experiments on benchmark datasets show SeD-UD outperforms current state-of-the-art models.
When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought
Authors: Yiyang Zhou; Haoqin Tu; Zijun Wang; Zeyu Wang; Niklas Muennighoff; Fan Nie; Chaorui Deng; Shen Yan; Haoqi Fan; Yejin Choi; James Zou; Cihang Xie; Huaxiu Yao; Qinghao Ye
Venue: CVPR 2026 (main)

Abstract: We propose MIRA (Multimodal Imagination for Reasoning Assessment), a new benchmark designed to evaluate models in scenarios where generating intermediate visual images is essential for successful reasoning. Unlike traditional Chain-of-thought (CoT) methods that rely solely on text, tasks in MIRA require models to generate and utilize intermediate images --- such as sketches, structural diagrams, or path drawings --- to guide their reasoning process. This setup closely mirrors how humans solve complex problems through "drawing to think". To solve this, MIRA focuses on tasks that are intrinsically challenging and involve complex structures, spatial relationships, or reasoning steps that are difficult to express through language alone (e.g., tracking a die's movement on a board and summing the face-down values after each roll). To ensure that our evaluation data is of high-quality, we include 546 multimodal problems, annotated with intermediate visual images and final answers. We also propose a unified evaluation protocol for MIRA that spans three levels of evaluation input: direct input with image and question only, text-only CoT (Text-CoT) input with image and thinking prompts, and Visual-CoT input with both annotated image clues and textual thinking prompts. To probe the upper bound of model capacity on our benchmark, we also report pass@k and majority voting accuracies under different k settings. Experimental results show that existing multimodal large language models (MLLMs), including strongest private models (e.g., GPT-5, o3, Gemini 2.5 Pro) as well as strong open-weight models (e.g., Qwen2.5-VL, GLM 4.5V), perform poorly when relying solely on textual prompts. However, when intermediate visual cues are provided, model performance improves consistently, yielding an average relative gain of 33.7% across all models and tasks. We also probe the upper bound by expanding the search space and designing textual prompts aligned with Visual-CoT, but both yield only limited improvements compared to our Visual-CoT setting. These results underscore the critical role of imagined visual information in enabling successful reasoning on MIRA.
Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
Authors: Jihang Wang; Dongcheng Zhao; Ruolin Chen; Qian Zhang; Yi Zeng
Venue: CVPR 2026 (main)

Abstract: Spiking Neural Networks (SNNs) utilize spike-based activations to mimic the brain's energy-efficient information processing. However, the binary and discontinuous nature of spike activations causes vanishing gradients, making adversarial robustness evaluation via gradient descent unreliable. While improved surrogate gradient methods have been proposed, their effectiveness under strong adversarial attacks remains unclear. We propose a more reliable framework for evaluating SNN adversarial robustness. We theoretically analyze the degree of gradient vanishing in surrogate gradients and introduce the Adaptive Sharpness Surrogate Gradient (ASSG), which adaptively evolves the shape of the surrogate function according to the input distribution during attack iterations, thereby enhancing gradient accuracy while mitigating gradient vanishing. In addition, we design an adversarial attack with adaptive step size under the L_infinity constraint--Stable Adaptive Projected Gradient Descent (SA-PGD), achieving faster and more stable convergence under imprecise gradients. Extensive experiments show that our approach substantially increases attack success rates across diverse adversarial training schemes, SNN architectures and neuron models, providing a more generalized and reliable evaluation of SNN adversarial robustness. The experimental results further reveal that the robustness of current SNNs has been significantly overestimated and highlighting the need for more dependable adversarial training methods. The code is released at https://github.com/craree/ASSG-SNNs-Robustness-Evaluation
Spectral Scalpel: Amplifying Adjacent Action Discrepancy via Frequency-Selective Filtering for Skeleton-Based Action Segmentation
Authors: Haoyu Ji; Bowen Chen; Zhihao Yang; Wenze Huang; Yu Gao; Xueting Liu; Weihong Ren; Zhiyong Wang; Honghai Liu
Venue: CVPR 2026 (main)

Abstract: Skeleton-based Temporal Action Segmentation (STAS) seeks to densely segment and classify diverse actions within long, untrimmed skeletal motion sequences. However, existing STAS methodologies face challenges of limited inter-class discriminability and blurred segmentation boundaries, primarily due to insufficient distinction of spatio-temporal patterns between adjacent actions. To address these limitations, we propose Spectral Scalpel, a frequency-selective filtering framework aimed at suppressing shared frequency components between adjacent distinct actions while amplifying their action-specific frequencies, thereby enhancing inter-action discrepancies and sharpening transition boundaries. Specifically, Spectral Scalpel employs adaptive multi-scale spectral filters as scalpels to edit frequency spectra, coupled with a discrepancy loss between adjacent actions serving as the surgical objective. This design amplifies representational disparities between neighboring actions, effectively mitigating boundary localization ambiguities and inter-class confusion. Furthermore, complementing long-term temporal modeling, we introduce a frequency-aware channel mixer to strengthen channel evolution by aggregating spectra across channels. This work presents a novel paradigm for STAS that extends conventional spatio-temporal modeling by incorporating frequency-domain analysis. Extensive experiments on five public datasets demonstrate that Spectral Scalpel achieves state-of-the-art performance. Code is available at https://github.com/HaoyuJi/SpecScalpel.
SemiGDA: Generative Dual-distribution Alignment for Semi-Supervised Medical Image Segmentation
Authors: Kaiwen Huang; Yi Zhou; Yizhe Zhang; Jingxiong Li; Tao Zhou
Venue: CVPR 2026 (main)

Abstract: Semi-supervised learning addresses label scarcity and high annotation costs in medical image segmentation by exploiting the latent information in unlabeled data to enhance model performance. Traditional discriminative segmentation relies on segmentation masks, neglecting feature-level distribution constraints. This limits robust semantic representation learning and adaptive modeling of unlabeled data in scenarios with few labels. To address these limitations, we propose SemiGDA, a novel Generative Dual-distribution Alignment framework for semi-supervised medical image segmentation. Our SemiGDA overcomes the reliance of discriminative methods on large labeled datasets by aligning feature and semantic distributions to boost semantic learning and scene adaptability. Specifically, we propose a Dual-distribution Alignment Module (DAM), which employs two structurally distinct encoders to model image and mask feature distributions. It enforces their alignment in the latent space via distributional constraints, establishing structured feature consistency. Moreover, we design a Consistency-Driven Skip Adapter (CDSA) strategy, which introduces dual skip adapters (Image and Mask) to fuse multi-scale features via skip connections. Using a consistency loss, CDSA enhances cross-branch semantic alignment and reinforces fine-grained semantic consistency. Experimental results on diverse medical datasets show that our method outperforms other state-of-the-art semi-supervised segmentation methods. Code is released at: https://github.com/taozh2017/SemiGDA.
DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance
Authors: Peiying Zhang; Nanxuan Zhao; Matthew Fisher; Yiran Xu; Jing Liao; Difan Liu
Venue: CVPR 2026 (main)

Abstract: Recent vision-language model (VLM)-based approaches have achieved impressive results on SVG generation. However, because they generate only text and lack visual signals during decoding, they often struggle with complex semantics and fail to produce visually appealing or geometrically coherent SVGs. We introduce DuetSVG, a unified multimodal model that jointly generates image tokens and corresponding SVG tokens in an end-to-end manner. DuetSVG is trained on both image and SVG datasets. At inference, we apply a novel test-time scaling strategy that leverages the model's native visual predictions as guidance to improve SVG decoding quality. Extensive experiments show that our method outperforms existing methods, producing visually faithful, semantically aligned, and syntactically clean SVGs across a wide range of applications.
FACE: A Face-based Autoregressive Representation for High-Fidelity and Efficient Mesh Generation
Authors: Hanxiao Wang; Yuan-Chen Guo; Ying-Tian Liu; Zi-Xin Zou; Biao Zhang; Weize Quan; Ding Liang; Yan-Pei Cao; Dong-Ming Yan
Venue: CVPR 2026 (main)

Abstract: Autoregressive models for 3D mesh generation suffer from a fundamental limitation: they flatten meshes into long vertex-coordinate sequences. This results in prohibitive computational costs, hindering the efficient synthesis of high-fidelity geometry. We argue this bottleneck stems from operating at the wrong semantic level. We introduce FACE, a novel Autoregressive Autoencoder (ARAE) framework that reconceptualizes the task by generating meshes at the face level. Our one-face-one-token strategy treats each triangle face, the fundamental building block of a mesh, as a single, unified token. This simple yet powerful design reduces the sequence length by a factor of nine, leading to an unprecedented compression ratio of 0.11, halving the previous state-of-the-art. This dramatic efficiency gain does not compromise quality; by pairing our face-level decoder with a powerful VecSet encoder, FACE achieves state-of-the-art reconstruction quality on standard benchmarks. The versatility of the learned latent space is further demonstrated by training a latent diffusion model that achieves high-fidelity, single-image-to-mesh generation. FACE provides a simple, scalable, and powerful paradigm that lowers the barrier to high-quality structured 3D content creation.