GenAI4ELab/papercli-papers huggingface dataset:
QD-PCQA: Quality-Aware Domain Adaptation for Point Cloud Quality Assessment
Authors: Guohua Zhang; Jian Jin; Meiqin Liu; Chao Yao; Weisi Lin
Venue: CVPR 2026 (main)

Abstract: No-Reference Point Cloud Quality Assessment (NR-PCQA) still struggles with generalization, primarily due to the scarcity of annotated point cloud datasets. Since the Human Visual System (HVS) drives perceptual quality assessment independently of media types, prior knowledge on quality learned from images can be repurposed for point clouds. This insight motivates adopting Unsupervised Domain Adaptation (UDA) to transfer quality-relevant priors from labeled images to unlabeled point clouds. However, existing UDA-based PCQA methods often overlook key characteristics of perceptual quality, such as sensitivity to quality ranking and quality-aware feature alignment, thereby limiting their effectiveness. To address these issues, we propose a novel Quality-aware Domain adaptation framework for PCQA, termed QD-PCQA. The framework comprises two main components: i) a Rank-weighted Conditional Alignment (RCA) strategy that aligns features under consistent quality levels and adaptively emphasizes misranked samples to reinforce perceptual quality ranking awareness; and ii) a Quality-guided Feature Augmentation (QFA) strategy, which includes quality-guided style mixup, multi-layer extension, and dual-domain augmentation modules to augment perceptual feature alignment. Extensive cross-domain experiments demonstrate that QD-PCQA significantly improves generalization in NR-PCQA tasks.
Bi-Bridge: Bidirectional Diffusion Bridges for Low-Light Image Enhancement
Authors: Zeyu Hua; Hui Li; Yu Wang; Song Wang; Congchao Zhu; Caixia Zheng
Venue: CVPR 2026 (main)

Abstract: Low-Light Image Enhancement (LLIE) is a challenging task, as severe information loss means a single input can correspond to multiple plausible restorations. This inherent ambiguity causes conventional regression-based models to produce overly-smooth results that lack detail. While recent generative models can create richer details, their common unidirectional design often compromises content fidelity by distorting original structures. We introduce Bi-Bridge, a unified framework that models both enhancement and its inverse degradation within a single symmetric diffusion bridge. By compelling the network to preserve essential content structures across both transformations, this bidirectional learning acts as a powerful constraint, leading to significantly more faithful and realistic restorations. Extensive experiments show that Bi-Bridge outperforms state-of-the-art (SOTA) methods across multiple benchmarks, establishing a new standard for fidelity and perceptual quality.
Gradient Knows Best: Mixed-Precision Quantization via Gradient-Guided Bit Allocation for Super-Resolution
Authors: Jun Young Kim; Joo Hyeon Jeon; Sangyeon Ahn; Yoonseo Park; Yong Seok Oh; Bogyeong Kim; Sung In Cho
Venue: CVPR 2026 (main)

Abstract: Although deep learning-based image super-resolution (SR) models have achieved remarkable progress in reconstruction quality, their high computational and memory demands make them unsuitable for lightweight platforms. To address this issue, various quantization techniques have been introduced. Among them, mixed-precision quantization (MPQ) introduces a layer-wise bit-width allocation to balance computational efficiency with reconstruction quality. However, existing MPQ methods based on post-training quantization (PTQ) for SR models face two critical limitations. First, quantization sensitivity estimation using static statistics fails to capture the accurate quantization error induced by each layer, resulting in suboptimal bit allocation. Second, removing batch normalization (BN) to preserve high-frequency details leads to scale inconsistencies across activations, making fixed quantization ranges insufficient to accurately represent their distribution. Therefore, we propose a novel PTQ-based MPQ framework tailored for SR models. Our method estimates the quantization sensitivity of weights and activations by leveraging gradients of the objective function with respect to bit-widths, enabling adaptive layer-wise bit allocation and fast convergence. Additionally, we introduce a dynamic activation range normalization that alleviates the distributional imbalance caused by the absence of BN, ensuring stable quantization under fixed range constraints. Our method outperforms existing PTQ-based methods by 1.26 dB in peak signal-to-noise ratio (PSNR) on the Urban100 dataset and reduces quantization time by x1.9 for 3-bit quantization of EDSR x4.
StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation
Authors: Mingyu Liu; Jiuhe Shu; Hui Chen; Zeju Li; Canyu Zhao; Jiange Yang; Shenyuan Gao; Hao Chen; Chunhua Shen
Venue: CVPR 2026 (main)

Abstract: A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and decision making. However, existing methods often fail to achieve this balance, yielding representations that are either overly redundant or lacking in task-critical information. We propose an unsupervised approach that learns a highly compressed two-token state representation using a lightweight encoder and a pre-trained Diffusion Transformer (DiT) decoder, capitalizing on its strong generative prior. Our representation is efficient, interpretable, and integrates seamlessly into existing VLA-based models, improving performance by 11.6% on LIBERO and 31% in real-world task success with minimal inference overhead. More importantly, we find that the difference between these tokens, obtained via latent interpolation, naturally serves as a highly effective latent action, which can be further decoded into executable robot actions. This emergent capability reveals that our representation captures structured dynamics without explicit supervision. We name our method StaMo for its ability to learn generalizable robotic Motion from compact State representation, which is encoded from static images, challenging the prevalent dependence to learning latent action on complex architectures and video data. The resulting latent actions also enhance policy co-training, outperforming prior methods by 10.4% with improved interpretability. Moreover, our approach scales effectively across diverse data sources, including real-world robot data, simulation, and human egocentric video.
Mind the Way You Select Negative Texts: Pursuing the Distance Consistency in OOD Detection with VLMs
Authors: Zhikang Xu; Qianqian Xu; Zitai Wang; Cong Hua; Sicong Li; Zhiyong Yang; Qingming Huang
Venue: CVPR 2026 (main)

Abstract: Out-of-distribution (OOD) detection seeks to identify samples from unknown classes, a critical capability for deploying machine learning models in open-world scenarios. Recent research has demonstrated that Vision-Language Models (VLMs) can effectively leverage their multi-modal representations for OOD detection. However, current methods often incorporate intra-modal distance during OOD detection, such as comparing negative texts with ID labels or comparing test images with image proxies. This design paradigm creates an inherent inconsistency against the inter-modal distance that CLIP-like VLMs are optimized for, potentially leading to suboptimal performance. To address this limitation, we propose InterNeg, a simple yet effective framework that systematically utilizes consistent inter-modal distance enhancement from textual and visual perspectives. From the textual perspective, we devise an inter-modal criterion for selecting negative texts. From the visual perspective, we dynamically identify high-confidence OOD images and invert them into the textual space, generating extra negative text embeddings guided by inter-modal distance. Extensive experiments across multiple benchmarks demonstrate the superiority of our approach. Notably, our InterNeg achieves state-of-the-art performance compared to existing works, with a 3.47% reduction in FPR95 on the large-scale ImageNet benchmark and a 5.50% improvement in AUROC on the challenging Near-OOD benchmark.
Pixels Don't Lie (But Your Detector Might): Bootstrapping MLLM-as-a-Judge for Trustworthy Deepfake Detection and Reasoning Supervision
Authors: Kartik Kuckreja; Parul Gupta; Muhammad Haris Khan; Abhinav Dhall
Venue: CVPR 2026 (main)

Abstract: Deepfake detection models often generate natural-language explanations, yet their reasoning is frequently ungrounded in visual evidence, limiting reliability. Existing evaluations measure classification accuracy but overlook reasoning fidelity. We propose DeepfakeJudge, a framework for scalable reasoning supervision and evaluation, that integrates an out-of-distribution benchmark containing recent generative and editing forgeries, a human-annotated subset with visual reasoning labels, and a suite of evaluation models, that specialize in evaluating reasoning rationales without the need for explicit ground truth reasoning rationales. The Judge is optimized through a bootstrapped generator-evaluator process that scales human feedback into structured reasoning supervision and supports both pointwise and pairwise evaluation. On the proposed meta-evaluation benchmark, our reasoning-bootstrapped model achieves an accuracy of 96.2%, outperforming \texttt 30x  larger baselines. The reasoning judge attains very high correlation with human ratings and 98.9% percent pairwise agreement on the human annotated meta-evaluation subset. These results establish reasoning fidelity as a quantifiable dimension of deepfake detection and demonstrate scalable supervision for interpretable deepfake reasoning. Our user study indicates that humans prefer reasonings generated by our framework 70% of the time, in faithfullness, groundedness and usefulness compared to other models and datasets. All of our datasets, models, and codebase will be open-sourced.
DualPrim: Compact 3D Reconstruction with Positive and Negative Primitives
Authors: Xiaoxu Meng; Zhongmin Chen; Bo Yang; Weikai Chen; Weixiao Liu; Lin Gao
Venue: CVPR 2026 (main)

Abstract: We present Compact 3D Reconstruction with Positive and Negative Primitives (DualPrim), a novel approach for reconstructing compact and topologically regular 3D meshes from multi-view images. Unlike traditional methods that rely on implicit representations such as signed distance functions, or explicit formats such as meshes and point clouds, our method models geometry using quadrics-based 3D primitives. Each primitive is defined by a positive-density superquadric that contributes to the shape, and a negative-density superquadric that carves out local volumes, enabling fine-grained geometric control and flexible topology. This dual-primitive representation yields compact, well-regularized, and efficiently parameterized mesh reconstructions. To infer primitive parameters from multi-view images, we design a differentiable rendering pipeline that jointly estimates positive and negative superquadrics under view-consistent supervision. Extensive experiments demonstrate that DualPrim outperforms state-of-the-art methods in reconstruction accuracy while producing more geometrically concise, interpretable, and high-fidelity 3D meshes.
TagSplat: Topology-Aware Gaussian Splatting for Dynamic Mesh Modeling and Tracking
Authors: Hanzhi Guo; Dongdong Weng; Mo Su; Yixiao Chen; Xiaonuo Dongye; Chenyu Xu
Venue: CVPR 2026 (main)

Abstract: Topology-consistent dynamic model sequences are essential for applications such as animation and model editing. However, existing 4D reconstruction methods face challenges in generating high-quality topology-consistent meshes. To address this, we propose a topology-aware dynamic reconstruction framework based on Gaussian Splatting. We introduce a Gaussian topological structure that explicitly encodes spatial connectivity. This structure enables topology-aware densification and pruning, preserving the manifold consistency of the Gaussian representation. Temporal regularization terms further ensure topological coherence over time, while differentiable mesh rasterization improves mesh quality. Experimental results demonstrate that our method reconstructs topology-consistent mesh sequences with significantly higher accuracy than existing approaches. Moreover, the resulting meshes enable precise 3D keypoint tracking.
RHCNet: Residual-Guided Hierarchical Calibration Network for Robust Underwater Object Detection
Authors: Yueying Wang; Yiteng Guo; Weidong Zhang; Jie Wen; Liquan Shen; Huaicheng Yan; Xin Xu
Venue: CVPR 2026 (main)

Abstract: Underwater images commonly suffer from foreground-background ambiguity, loss of structural details, and severely reduced contrast, which collectively make underwater object detection (UOD) an inherently challenging task. To handle this issue, we present a residual-guided hierarchical calibration network (RHCNet) designed to achieve more efficient and robust UOD, which comprises a residual-guided feature enhancement module (RGFE) and a hierarchical feature calibration pyramid module (HFCP). Concretely, RHCNet extends the standard ResNet-50 backbone by embedding the RGFE, which effectively strengthens the representation of edge and texture features in blurry regions by jointly leveraging convolutional operations and attention mechanisms to achieve more discriminative feature extraction for UOD. Subsequently, the HFCP integrates a bottom-up semantic enhancement path and a top-down fine-grained feature compensation path, while a K-means clustering-guided feature calibration module is jointly employed to ensure multi-level cross-scale semantic consistency and accurate alignment of salient region features. Extensive experiments on the DUO and UTDAC benchmark datasets demonstrated that our RHCNet attains the highest AP scores of 70.53% and 53.35%, respectively. Besides, our RHCNet also maintains excellent detection accuracy and strong generalization capability on the COCO dataset for terrestrial scenarios. The code is available at https://github.com/YitengGuo/RHCNet.
VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments
Authors: Zelai Xu; Zhexuan Xu; Xiangmin Yi; Huining Yuan; Mo Guang; Kaiwen Long; Xinlei Chen; Yi Wu; Chao Yu; Yu Wang
Venue: CVPR 2026 (main)

Abstract: Recent advancements in Vision Language Models (VLMs) have expanded their capabilities to interactive agent tasks, yet existing benchmarks remain limited to single-agent or text-only environments. In contrast, real-world scenarios often involve multiple agents interacting within rich visual and textual contexts, posing challenges with both multimodal observations and strategic interactions. To bridge this gap, we introduce Visual Strategic Bench (VS-Bench), a multimodal benchmark that evaluates VLMs for strategic abilities in multi-agent environments. VS-Bench comprises ten vision-grounded environments that cover cooperative, competitive, and mixed-motive interactions. The performance of VLM agents is evaluated across three dimensions: perception measured by element recognition accuracy; strategic reasoning measured by next-action prediction accuracy; and decision-making measured by normalized episode return. Extensive experiments on fifteen leading VLMs show that, although current models exhibit strong perception abilities, there remains a significant gap to optimal performance in reasoning and decision-making, with the best-performing model attaining 46.6% prediction accuracy and 31.4% normalized return. We further analyze the key factors influencing performance, conduct human studies, and examine failure modes to provide a deeper understanding of VLMs' strategic abilities. By standardizing the evaluation and highlighting the limitations of existing models, we envision VS-Bench as a foundation for future research on strategic multimodal agents.
Machine Unlearning via Adaptive Gradient Reweighting and Multi-stage Objective Optimization
Authors: Juxin Lu; Haoyu Shi; Mengyao Wang; Huaiwen Zhang
Venue: CVPR 2026 (main)

Abstract: Machine Unlearning (MU) focuses on removing the influence of training samples from pre-trained models without retraining the model entirely. Existing MU methods have made several efforts to enable complete forgetting while preserving the model's performance on remaining data. However, they typically apply equal weights across different data, overlooking the ambiguous decision boundaries between similar samples or approximate classes. This leads to unnecessary consumption of shallowly memorized samples and significant performance degradation for approximate retention classes. Additionally, the inherent inconsistency between forgetting and retention objectives results in gradient conflict and domination problems during training, hindering model convergence and degrading overall performance. To address these, we introduce a novel adaptive gradient reweighting that assigns importance weights to individual forget samples or vulnerable retention classes, thereby enabling more efficient unlearning and preserving the performance of approximate classes. Subsequently, we propose a multi-stage objective optimization strategy, which comprises three optimization stages: Direction Rectification, Temporal Stabilization, and Adaptive Objective Combination. This strategy rectifies the direction of conflicting gradients and prevents one task (forgetting or retention) from dominating the model update. Comprehensive analyses and extensive experiments on multiple public datasets demonstrate that our method achieves considerable performance improvements in various tasks and scenarios.
CURVE: A Benchmark for Cultural and Multilingual Long Video Reasoning
Authors: Darshan Singh; Arsha Nagrani; Kawshik Manikantan; Harman Singh; Dinesh Tewari; Tobias Weyand; Cordelia Schmid; Anelia Angelova; Shachi Dave
Venue: CVPR 2026 (main)

Abstract: Recent advancements in video models have shown tremendous progress, particularly in long video understanding. However, current benchmarks predominantly feature western-centric data and English as the dominant language, introducing significant biases in evaluation. To address this, we introduce CURVE, a challenging benchmark for multicultural and multilingual video reasoning. CURVE comprises high-quality, entirely human-generated annotations from diverse, region-specific cultural videos across 18 global locales. Unlike prior work that relies on automatic translations, CURVE provides complex questions, answers, and multi-step reasoning steps, all crafted in native languages. Making progress on CURVE requires a deeply situated understanding of visual cultural context. Furthermore, we leverage CURVE's reasoning traces to construct evidence-based graphs and propose a novel iterative strategy using these graphs to identify fine-grained errors in reasoning. Our evaluations reveal that SoTA Video-LLMs struggle significantly, performing substantially below human-level accuracy, with errors primarily stemming from the visual perception of cultural elements. We will release CURVE to foster the development of more equitable and capable multimodal foundation models.
Diffusion-Based Native Adversarial Synthesis for Enhanced Medical Segmentation Generalization
Authors: Hongyu Zhang; Haipeng Chen; Zhimin Xu; Chengxin Yang; Yingda Lyu
Venue: CVPR 2026 (main)

Abstract: Diffusion models (DMs) can generate anatomically realistic medical images, offering a compelling route to improving generalization through synthetic augmentation. Yet high visual realism does not necessarily translate into improved downstream utility. This work addresses two key questions in diffusion-driven augmentation. First, what should be synthesized? We show that synthetic adversariality, namely the expected empirical loss induced by synthetic samples, is a key driver of generalization. More importantly, only native adversariality, arising from hard examples supported by the diffusion model distribution, yields consistent gains, whereas artificial adversariality induced by attack-based perturbations is detrimental. Second, how should such samples be synthesized? We propose the Adversariality Miner, a lightweight module that optimizes the initial noise to mine natively adversarial samples without modifying or retraining the diffusion model. Extensive experiments across diverse diffusion backbones and medical benchmarks confirm the effectiveness of our approach, establishing a principled path toward diffusion-driven generalization.
Proxy-Tuning: Tailoring Multimodal Autoregressive Models for Subject-Driven Image Generation
Authors: Yi Wu; Shengju Qian; Lingting Zhu; Lei Liu; Wandi Qiao; Ziqiang Li; Lequan Yu; Bin Li
Venue: CVPR 2026 (main)

Abstract: Multimodal autoregressive (AR) models, based on next-token prediction and transformer architecture, have demonstrated remarkable capabilities in various multimodal tasks including text-to-image (T2I) generation. Despite their strong performance in general T2I tasks, our research reveals that these models initially struggle with subject-driven image generation compared to dominant diffusion models. To address this limitation, we introduce Proxy-Tuning, leveraging diffusion models to enhance AR models' capabilities in subject-specific image generation. Our method reveals a striking weak-to-strong phenomenon: fine-tuned AR models consistently outperform their diffusion model supervisors in both subject fidelity and prompt adherence. We analyze this performance shift and identify scenarios where AR models excel, particularly in multi-subject compositions and contextual understanding. This work not only demonstrates impressive results in subject-driven AR image generation, but also unveils the potential of weak-to-strong generalization in the image generation domain, contributing to a deeper understanding of different architectures' strengths and limitations.
CLIPoint3D: Language-Grounded Few-Shot Unsupervised 3D Point Cloud Domain Adaptation
Authors: Mainak Singha; Sarthak Mehrotra; Paolo Casari; Subhasis Chaudhuri; Elisa Ricci; Biplab Banerjee
Venue: CVPR 2026 (main)

Abstract: Recent vision-language models (VLMs) such as CLIP demonstrate impressive cross-modal reasoning, extending beyond images to 3D perception. Yet, these models remain fragile under domain shifts, especially when adapting from synthetic to real-world point clouds. Conventional 3D domain adaptation approaches rely on heavy trainable encoders, yielding strong accuracy but at the cost of efficiency. We introduce CLIPoint3D, the first framework for few-shot unsupervised 3D point cloud domain adaptation built upon CLIP. Our approach projects 3D samples into multiple depth maps and exploits the frozen CLIP backbone, refined through a knowledge-driven prompt tuning scheme that integrates high-level language priors with geometric cues from a lightweight 3D encoder. To adapt task-specific features effectively, we apply parameter-efficient fine-tuning to CLIP's encoders and design an entropy-guided view sampling strategy for selecting confident projections. Furthermore, an optimal transport-based alignment loss and an uncertainty-aware prototype alignment loss collaboratively bridge source-target distribution gaps while maintaining class separability. Extensive experiments on PointDA-10 and GraspNetPC-10 benchmarks show that CLIPoint3D achieves consistent 3-16% accuracy gains over both CLIP-based and conventional encoder-based baselines. Project page: https://sarthakm320.github.io/CLIPoint3D.