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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:67f649f06a5ab2ada16ea645a843d028CVF · CVPR 2026

Bypassing the Transport Plan: Dynamic Reweighting for Out-of-Distribution Detection with Optimal Transport

Xiao, Yang, Liu, Weiming, Dan, Jun, Xu, Tengyue, Wang, Fan, Yu, Hua, Dong, Junhao, Liu, Jiao 等 10 位作者

原文摘要

Semi-supervised learning (SSL) has achieved remarkable progress by leveraging both limited labeled data and abundant unlabeled data. However, unlabeled datasets often contain out-of-distribution (OOD) samples from unknown classes, which can lead to performance degradation in open-set SSL scenarios. Current OOD detection methods are constrained by the absence of labeled OOD samples. Although optimal transport (OT) has proven to be effective to provide pseudo OOD scores for supervised learning, it still faces two main challenges, i.e., the unavoidable problem of finding the optimal transport plan and the unreliable OOD score caused by dense solutions. To overcome thess limitations, we propose a novel open-set OOD detection model named **DREW**, which leverages **D**ynamic **REW**eighting approach for OT-based OOD detection. Specifically, we start by formulating OOD detection as a semi-unbalanced optimal transport (SemiUOT) problem. The proposed DREW model can dynamically transform SemiUOT into the classical OT formula and then directly obtain the pseudo OOD score from the new source distribution weights. Contrary to existing OT-based methods, DREW provides theoretically grounded and more accurate pseudo OOD scores, while avoiding the direct computation of the transport plan. Empirical results demonstrate the superiority of DREW in terms of both accuracy and efficiency. Extensive analytical experiments are conducted to elucidate the properties of each component.

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:7d9cdeb50138a9e5144dac2f1cc7135fCVF · CVPR 2026

UniChange: Unifying Change Detection with Multimodal Large Language Model

Zhang, Xu, Li, Danyang, Dong, Xiaohang, Wu, Tianhao, Yu, Hualong, Wang, Jianye, Li, Qicheng, Li, Xiang

原文摘要

Change detection (CD) is a fundamental task for monitoring and analysing land cover dynamics. While recent high performance models and high quality datasets have significantly advanced the field, a critical limitation persists. Current models typically acquire limited knowledge from single-type annotated data and cannot concurrently leverage diverse binary change detection (BCD) and semantic change detection (SCD) datasets. This constraint leads to poor generalisation and limited versatility. The recent advancements in Multimodal Large Language Models (MLLMs) introduce new possibilities for a unified CD framework. We leverage the language priors and unification capabilities of MLLMs to develop UniChange, the first MLLM-based unified change detection model. UniChange integrates generative language abilities with specialised CD functionalities. We introduce three special tokens: [T1], [T2], and [CHANGE], utilising their embeddings as the key to query variations. This approach successfully accommodates both BCD and SCD tasks. Furthermore, UniChange utilises text prompts to guide the identification of change categories, eliminating the reliance on predefined classification heads. This design allows UniChange to effectively acquire knowledge from multi-source datasets, even when their class definitions conflict. Experiments on four public benchmarks (WHU-CD, S2Looking, LEVIR-CD+, and SECOND) demonstrate SOTA performance, achieving IoU scores of 90.41, 53.04, 78.87, and 57.62, respectively, surpassing all previous methods. The code is available at https://github.com/NKU-HLT/UniChange.

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:de142307f16b58fa821b79a398c77b49CVF · CVPR 2026

Lifting Unlabeled Internet-level Data for 3D Scene Understanding

Chen, Yixin, Zhang, Yaowei, Yu, Huangyue, He, Junchao, Wang, Yan, Huang, Jiangyong, Shen, Hongyu, Ni, Junfeng 等 12 位作者

原文摘要

Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled videos are readily available on the internet. In this paper, we demonstrate that carefully designed data engines can leverage web-curated, unlabeled videos to automatically generate training data, to facilitate end-to-end models in 3D scene understanding alongside human-annotated datasets. We identify and analyze bottlenecks in automated data generation, revealing critical factors that determine the efficiency and effectiveness of learning from unlabeled data. To validate our approach across different perception granularities, we evaluate on three tasks spanning low-level perception, i.e., 3D object detection and instance segmentation, to high-level reasoning, i.e., 3D spatial Visual Question Answering (VQA) and Vision-Lanugage Navigation (VLN). Models trained on our generated data demonstrate strong zero-shot performance and show further improvement after finetuning. This demonstrates the viability of leveraging readily available web data as a path toward more capable scene understanding systems.

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:f81bb8960992720d02a321b70a6ca106CVF · CVPR 2026

Dr.Occ: Depth- and Region-Guided 3D Occupancy from Surround-View Cameras for Autonomous Driving

Zhu, Xubo, Zhang, Haoyang, He, Fei, Wu, Rui, Shan, Yanhu, Yang, Wen, Yu, Huai

原文摘要

3D semantic occupancy prediction is crucial for autonomous driving perception, offering comprehensive geometric scene understanding and semantic recognition. However, existing methods struggle with geometric misalignment in view transformation due to lack of pixel-level accurate depth estimation, and severe spatial class imbalance where semantic categories exhibit strong spatial anisotropy. To address these challenges, we propose Dr.Occ, a depth- and region-guided occupancy prediction framework. Specifically, we introduce a depth-guided 2D-to-3D View Transformer (D-VFormer) that effectively leverages high-quality dense depth cues from MoGe-2 to construct reliable geometric priors, thereby enabling precise geometric alignment of voxel features. Moreover, inspired by the Mixture-of-Experts (MoE) framework, we propose a region-guided Expert Transformer (R/R-EFormer) that adaptively allocates region-specific experts to focus on different spatial regions, effectively addressing spatial semantic variations. Thus, the two components make complementary contributions: depth guidance ensures geometric alignment, while region experts enhance semantic learning. Experiments on the Occ3D--nuScenes benchmark demonstrate that Dr.Occ improves the strong baseline BEVDet4D by 7.43% mIoU and 3.09% IoU under the full vision-only setting.

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 ↗