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首次观测 · 记录 #1CVF · CVPR 2026:cvf:15167653bb0ceb523a2d97233c2d9994CVF · CVPR 2026

BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models

Wang, Shengao, Wang, Wenqi, Wang, Zecheng, Whitton, Max, Wakeham, Michael, Chandra, Arjun, Huang, Joey, Zhu, Pengyue 等 23 位作者

原文摘要

Early children's developmental trajectories set up a natural goal for sample-efficient pretraining of vision foundation models. We introduce BabyVLM-V2, a developmentally grounded framework for infant-inspired vision-language modeling that extensively improves upon BabyVLM-V1 through a longitudinal, multifaceted pretraining set, a versatile model, and, most importantly, DevCV Toolbox for cognitive evaluation. The pretraining set maximizes coverage while minimizing curation of a longitudinal, infant-centric audiovisual corpus, yielding video-utterance, image-utterance, and multi-turn conversational data that mirror infant experiences. DevCV Toolbox adapts all vision-related measures of the recently released NIH Baby Toolbox into a benchmark suite of ten multimodal tasks, covering spatial reasoning, memory, and vocabulary understanding aligned with early children's capabilities. Experimental results show that a compact model pretrained from scratch can achieve competitive performance on DevCV Toolbox, outperforming GPT-4o on some tasks. We hope the principled, unified BabyVLM-V2 framework will accelerate research in developmentally plausible pretraining of vision foundation models.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:219d067f4cae4d7ffa97a1d652be7c6aCVF · CVPR 2026

Disentangling to Re-couple: Resolving the Similarity-Controllability Paradox in Subject-Driven Text-to-Image Generation

Li, Shuang, Deng, Chao, Chen, Hang, Liu, Liqun, Hu, Zhenyu, Cao, Te, Xue, Mengge, Chen, Yuan 等 11 位作者

原文摘要

Subject-Driven Text-to-Image (T2I) Generation aims to preserve a subject's identity while editing its context based on a text prompt. A core challenge in this task is the "similarity-controllability paradox", where enhancing textual control often degrades the subject's fidelity, and vice-versa. We argue this paradox stems from the ambiguous role of text prompts, which are often tasked with describing both the subject and the desired modifications, leading to conflicting signals for the model. To resolve this, we propose DisCo, a novel framework that first Disentangles and then re-Couples visual and textual information. First, our textual-visual decoupling module isolates the sources of information: subject identity is extracted exclusively from the reference image with the entity word of the subject, while the text prompt is simplified to contain only the modification command, where the subject refers to general pronouns, eliminating descriptive ambiguity. However, this strict separation can lead to unnatural compositions between the subject and its contexts. We address this by designing a dedicated reward signal and using reinforcement learning to seamlessly recouple the visually-defined subject and the textually-generated context. Our approach effectively resolves the paradox, enabling simultaneous high-fidelity subject preservation and precise textual control. Extensive experiments demonstrate that our method achieves state-of-the-art performance, producing highly realistic and coherent images.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:3292c2ddfa854edb08cc45737de49b1aCVF · CVPR 2026

MatMart: Material Reconstruction of 3D Objects via Diffusion

Wu, Xiuchao, Zhu, Pengfei, Lyu, Jiangjing, Liu, Xinguo, Guo, Jie, Guo, Yanwen, Xu, Weiwei, Lyu, Chengfei

原文摘要

Applying diffusion models to physically-based material estimation and generation has recently gained prominence. In this paper, we propose MatMart, a novel material reconstruction framework for 3D objects, offering the following advantages. First, MatMart adopts a two-stage reconstruction, starting with accurate material prediction from inputs and followed by prior-guided material generation for unobserved views, yielding high-fidelity results. Second, by utilizing progressive inference alongside the proposed view-material cross-attention (VMCA), MatMart enables reconstruction from an arbitrary number of input images, demonstrating strong scalability and flexibility. Finally, MatMart achieves both material prediction and generation capabilities through end-to-end optimization of a single diffusion model, without relying on additional pre-trained models, thereby exhibiting enhanced stability across various types of objects. Extensive experiments demonstrate that MatMart achieves superior performance in material reconstruction compared to existing methods.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:3a97a84477f5d57b8cd74ce77e01c23eCVF · CVPR 2026

Bootstrapping Multi-view Learning for Test-time Noisy Correspondence

He, Changhao, Xue, Di, Li, Shuxian, Hao, Yanji, Peng, Xi, Hu, Peng

原文摘要

Multi-view learning fuses complementary views to improve perception, but real-world deployments often suffer from Test-time Noisy Correspondence (TNC) -- cross-view misalignment caused by asynchronous sampling, transient network congestion, or other disturbances. Such misalignment introduces semantic inconsistency and significantly degrades performance. Existing remedies typically estimate view-specific reliability from clean, well-aligned training data and then extrapolate to noisy fusion at inference, resulting in a train-test task gap and reduced robustness against TNC. To bridge this gap, we propose \underline \textcolor red B ootstrapping \underline \textcolor red M ulti-view \underline \textcolor red L earning (BML) -- a plug-and-play framework that explicitly learns to fuse under TNC. Specifically, BML performs in-place TNC bootstrapping to construct a controllable noise-augmented training set that simulates realistic correspondence distortion, thereby eliminating the task gap without external data. Unlike prior uncertainty-based approaches that model reliability in an unsupervised manner, BML presents a reveal-supervised paradigm, wherein a lightweight estimator jointly models intra-view predictive uncertainty (view quality) and inter-view prediction discrepancy (correspondence consistency) to produce calibrated reliability weights guided by both task objectives and bootstrapped supervision. Once deployed, these reliability weights directly modulate fusion, suppressing corrupted views while preserving informative ones. Across 11 benchmarks spanning diverse noise ratios, BML consistently outperforms state-of-the-art baselines and maintains robustness against TNC.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a674080e8d0a7dff4a80f265ce0ddb1aCVF · CVPR 2026

Ego-Grounding for Personalized Question-Answering in Egocentric Videos

Xiao, Junbin, Zhang, Shenglang, Zhu, Pengxiang, Yao, Angela

原文摘要

We present the first systematic analysis of multimodal large language models (MLLMs) in personalized question-answering requiring ego-grounding - the ability to understand the camera-wearer in egocentric videos. To this end, we introduce MyEgo, the first egocentric VideoQA dataset designed to evaluate MLLMs' ability to understand, remember, and reason about the camera wearer. MyEgo comprises 541 long videos and 5K personalized questions asking about "my things", "my activities", and "my past". Benchmarking reveals that competitive MLLMs across variants, including open-source vs. proprietary, thinking vs. non-thinking, small vs. large scales all struggle on MyEgo. Top closed- and open-source models (e.g., GPT-5 and Qwen3-VL) achieve only 46% and 36% accuracy, trailing human performance by near 40% and 50% respectively. Surprisingly, neither explicit reasoning nor model scaling yield consistent improvements. Models improve when relevant evidence is explicitly provided, but gains drop over time, indicating limitations in tracking and remembering "me" and "my past". These findings collectively highlight the crucial role of ego-grounding and long-range memory in enabling personalized QA in egocentric videos. We hope MyEgo and our analyses catalyze further progress in these areas for egocentric personalized assistance.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a9a9dca5b50c1fb310042c44a46d8276CVF · CVPR 2026

Thinking with Drafts: Speculative Temporal Reasoning for Efficient Long Video Understanding

Hu, Pengfei, Cao, Meng, Wang, Yingyao, Wang, Yi, Dong, Jiahua, Song, Jun, Cheng, Yu, Zheng, Bo 等 9 位作者

原文摘要

Long video understanding is essential for human-like intelligence, enabling coherent perception and reasoning over extended temporal contexts. While the emerging thinking-with-frames paradigm--which alternates between global temporal reasoning and local frame examination--has advanced the reasoning capabilities of video multi-modal large language models (MLLMs), it suffers from a significant efficiency bottleneck due to the progressively growing and redundant multi-modal context. To address this, we propose SpecTemp, a reinforcement learning-based Speculative Temporal reasoning framework that decouples temporal perception from reasoning via a cooperative dual-model design. In SpecTemp, a lightweight draft MLLM rapidly explores and proposes salient frames from densely sampled temporal regions, while a powerful target MLLM focuses on temporal reasoning and verifies the draft's proposals, iteratively refining its attention until convergence. This design mirrors the collaborative pathways of the human brain, balancing efficiency with accuracy. To support training, we construct the SpecTemp-80K dataset, featuring synchronized dual-level annotations for coarse evidence spans and fine-grained frame-level evidence. Experiments across multiple video understanding benchmarks demonstrate that SpecTemp not only maintains competitive accuracy but also significantly accelerates inference compared with existing thinking-with-frames methods.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:c341688da55014599aaf64faad8b128fCVF · CVPR 2026

SGAD-SLAM: Splatting Gaussians at Adjusted Depth for Better Radiance Fields in RGBD SLAM

Hu, Pengchong, Han, Zhizhong

原文摘要

3D Gaussian Splatting (3DGS) has made remarkable progress in RGBD SLAM. Current methods usually use 3D Gaussians or view-tied 3D Gaussians to represent radiance fields in tracking and mapping. However, these Gaussians are either too flexible or too limited in movements, resulting in slow convergence or limited rendering quality. To resolve this issue, we adopt pixel-aligned Gaussians but allow each Gaussian to adjust its position along its ray to maximize the rendering quality, even if Gaussians are simplified to improve system scalability. To speed up the tracking, we model the depth distribution around each pixel as a Gaussian distribution, and then use these distributions to align each frame to the 3D scene quickly. We report our evaluations on widely used benchmarks, justify our designs, and show advantages over the latest methods in view rendering, camera tracking, runtime, and storage complexity. Please see our project page for code and videos at https://machineperceptionlab.github.io/SGAD-SLAM-Project.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d417b7b1b4eb08297340745d6f3477b7CVF · CVPR 2026

Hyperbolic Relational Prompts for Intersectional Fairness in Medical VLMs

Qian, Jiayu, Yang, Zongxian, Chen, Guanxing, Hu, Pengwei, Tan, KC, Wang, Yan, Huang, Yu-An, Huang, Zhi-An

原文摘要

Ensuring fairness in medical vision-language models (VLMs) is essential for equitable healthcare, yet existing models amplify biases across demographic subgroups such as race and gender. Traditional fairness mitigation approaches relying on broad distribution alignment, fall short in addressing these nuanced intersectional disparities. We propose fairness-aware relational prompting (FRP), a novel framework that reformulates prompt generation as a dynamic, fairness-aware reasoning process. FRP constructs a relational graph to capture fine-grained, sample-level similarities and employs a hyperbolic graph layer to explicitly model the hierarchical structure of intersectional identities. Leveraging hyperbolic geometry enables reasoning over complex attribute combinations, effectively reducing entrenched biases. Evaluations on the FairVLMed and Harvard-GF datasets demonstrate that FRP achieves state-of-the-art diagnostic performance, with an area under the curve of 77.50% and 85.94% respectively, while substantially improving the demographic parity difference and equalized odds difference.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:deee6701b5ec1d0d1d2eeb8f21eec63eCVF · CVPR 2026

Beyond Ground-Truth: Leveraging Image Quality Priors for Real-World Image Restoration

Xiao, Fengyang, Hu, Peng, Xu, Lei, Guo, XingE, Qin, Guanyi, Shen, Yuqi, Fang, Chengyu, Zhang, Rihan 等 10 位作者

原文摘要

Real-world image restoration aims to restore high-quality (HQ) images from degraded low-quality (LQ) inputs captured under uncontrolled conditions. Existing methods typically depend on ground-truth (GT) supervision, assuming that GT provides perfect reference quality. However, GT can still contain images with inconsistent perceptual fidelity, causing models to converge to the average quality level of the training data rather than achieving the highest perceptual quality attainable. To address these problems, we propose a novel framework, termed IQPIR, that introduces an Image Quality Prior (IQP)--extracted from pre-trained No-Reference Image Quality Assessment (NR-IQA) models--to guide the restoration process toward perceptually optimal outputs explicitly. Our approach synergistically integrates IQP with a learned codebook prior through three key mechanisms:(1) a quality-conditioned Transformer, where NR-IQA-derived scores serve as conditioning signals to steer the predicted representation toward maximal perceptual quality. This design provides a plug-and-play enhancement compatible with existing restoration architectures without structural modification; and(2) a dual-branch codebook structure, which disentangles common and HQ-specific features, ensuring a comprehensive representation of both generic structural information and quality-sensitive attributes; and (3) a discrete representation-based quality optimization strategy, which mitigates over-optimization effects commonly observed in continuous latent spaces. Extensive experiments on real-world image restoration demonstrate that our method not only surpasses cutting-edge methods but also serves as a generalizable quality-guided enhancement strategy for existing methods. The code is available at https://github.com/fengyang1399-pixel/IQPIR.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:e5a00cdb4924b9689234d441454c67c5CVF · CVPR 2026

Next-Scale Prediction: A Self-Supervised Approach for Real-World Image Denoising

Shan, Yiwen, Zhao, Haiyu, Hu, Peng, Peng, Xi, Gou, Yuanbiao

原文摘要

Self-supervised real-world image denoising remains a fundamental challenge, arising from the antagonistic trade-off between decorrelating spatially structured noise and preserving high-frequency details. Existing blind-spot network (BSN) methods rely on pixel-shuffle downsampling (PD) to decorrelate noise, but aggressive downsampling fragments fine structures, while milder downsampling fails to remove correlated noise. To address this, we introduce Next-Scale Prediction (NSP), a novel self-supervised paradigm that decouples noise decorrelation from detail preservation. NSP constructs cross-scale training pairs, where BSN takes low-resolution, fully decorrelated sub-images as input to predict high-resolution targets that retain fine details. As a by-product, NSP naturally supports super-resolution of noisy images without retraining or modification. Extensive experiments demonstrate that NSP achieves state-of-the-art self-supervised denoising performance on real-world benchmarks, significantly alleviating the long-standing conflict between noise decorrelation and detail preservation. The code is available at https://github.com/XLearning-SCU/2026-CVPR-NSP.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗