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首次观测 · 记录 #1CVF · CVPR 2026:cvf:1129f838fa9cb3434f26c39cfcdf17d2CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Physically Ground Commonsense Knowledge for Articulated Object Manipulation with Analytic Concepts

Wei, Jiude, Li, Yuxuan, Lu, Cewu, Sun, Jianhua

原文摘要

We humans rely on a wide range of commonsense knowledge to interact with an extensive number and categories of objects in the physical world. Likewise, such commonsense knowledge is also crucial for robots to successfully develop generalized object manipulation skills. While recent advancements in Multi-modal Large Language Models (MLLMs) have showcased their impressive capabilities in acquiring commonsense knowledge and conducting commonsense reasoning, effectively grounding this semantic-level knowledge produced by MLLMs to the physical world to thoroughly guide robots in generalized articulated object manipulation remains a challenge that has not been sufficiently addressed. To this end, we introduce analytic concepts, procedurally defined upon mathematical symbolism that can be directly computed and simulated by machines. By leveraging the analytic concepts as a bridge between the semantic-level knowledge inferred by MLLMs and the physical world where real robots operate, we can identify the knowledge of object structure and functionality with physics-informed representations, and then use the physically grounded knowledge to instruct robot control policies for generalized and accurate articulated object manipulation. Extensive experiments in both real world and simulation demonstrate the superiority of our approach.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:152958bc0deb66e8b4ad3c79a9ea2ab6CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

X-PCR: A Benchmark for Cross-modality Progressive Clinical Reasoning in Ophthalmic Diagnosis

Wang, Gui, Zhong, Zehao, Zhou, YongSong, Li, Yudong, Wu, Ende, Cheah, Wooi Ping, Qu, Rong, Ren, Jianfeng 等 9 位作者

原文摘要

Despite significant progress in Multi-modal Large Language Models (MLLMs), their clinical reasoning capacity for multi-modal diagnosis remains largely unexamined. Current benchmarks, mostly single-modality data, can't evaluate progressive reasoning and cross-modal integration essential for clinical practice. We introduce the Cross-Modality Progressive Clinical Reasoning (X-PCR) benchmark, the first comprehensive evaluation of MLLMs through a complete ophthalmology diagnostic workflow, with two reasoning tasks: 1) a six-stage progressive reasoning chain spanning image quality assessment to clinical decision-making, and 2) a cross-modality reasoning task integrating six imaging modalities. The benchmark comprises 26,415 images and 177,868 expert-verified VQA pairs curated from 51 public datasets, covering 52 ophthalmic diseases. Evaluation of 21 MLLMs reveals critical gaps in progressive reasoning and cross-modal integration. Dataset and code: https://github.com/CVI-SZU/X-PCR.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:1ddff4cd1200161c671008c0e4dea7d3CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe

Yu, Tianyu, Wang, Zefan, Wang, Chongyi, Huang, Fuwei, Ma, Wenshuo, He, Zhihui, Cai, Tianchi, Chen, Weize 等 34 位作者

原文摘要

Multimodal Large Language Models (MLLMs) are undergoing rapid progress and represent the frontier of AI development. However, their training and inference efficiency have emerged as a core bottleneck in making MLLMs more accessible and scalable. To address the challenges, we present MiniCPM-V 4.5, an 8B parameter model designed for high efficiency and strong performance. We introduce three core improvements in model architecture, data strategy and training method: a unified 3D-Resampler model architecture for highly compact encoding over images and videos, a unified learning paradigm for document knowledge and text recognition without heavy data engineering, and a hybrid reinforcement learning strategy for proficiency in both short and long reasoning modes. Comprehensive experimental results in OpenCompass evaluation show that MiniCPM-V 4.5 surpasses widely used proprietary models such as GPT-4o-latest, and significantly larger open-source models such as Qwen2.5-VL 72B. Notably, the strong performance is achieved with remarkable efficiency. For example, on the widely adopted VideoMME benchmark, MiniCPM-V 4.5 surpasses Qwen2.5-VL 7B by +2.8, using just 46.7% GPU memory and 8.7% inference time.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:245fce70a9a28a97ed0b75f39d0fc55dCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Relational Visual Similarity

Nguyen, Thao, Mo, Sicheng, Singh, Krishna Kumar, Wang, Yilin, Shi, Jing, Kolkin, Nicholas, Shechtman, Eli, Lee, Yong Jae 等 9 位作者

原文摘要

Humans do not just see attribute similarity -- we also see relational similarity. An apple is like a peach because both are reddish fruit, but the Earth is also like a peach: its crust, mantle, and core correspond to the peach's skin, flesh, and pit. This ability to perceive and recognize relational similarity, is arguable by cognitive scientist to be what distinguishes humans from other species. Yet, all widely used visual similarity metrics today (e.g., LPIPS, CLIP, DINO) focus solely on perceptual attribute similarity and fail to capture the rich, often surprising relational similarities that humans perceive. How can we go beyond the visible content of an image to capture its relational properties? How can we bring images with the same relational logic closer together in representation space? To answer these questions, we first formulate relational image similarity as a measurable problem: two images are relationally similar when their internal relations or functions among visual elements correspond, even if their visual attributes differ. We then curate 114k image-caption dataset in which the captions are anonymized -- describing the underlying relational logic of the scene rather than its surface content. Using this dataset, we finetune a Vision-Language model to measure the relational similarity between images. This model serves as the first step toward connecting images by their underlying relational structure rather than their visible appearance. Our study shows that while relational similarity has a lot of real-world applications, existing image similarity models fail to capture it -- revealing a critical gap in visual computing.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:2ed3d0661b614c00759dd2bde6c61adaCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

GeoCoT: Towards Reliable Remote Sensing Reasoning with Manifold Perspective

Li, Daixun, Li, Zirui, He, Sibo, Tian, Jiayun, Cao, Mingxiang, Xie, Weiying, Wang, Yunke, Zhang, Xin 等 12 位作者

原文摘要

Multimodal Large Language Models (MLLMs) have shown strong potential in remote sensing (RS) through multi-task reasoning and cross-modal generalization.However, existing RS-MLLMs mainly rely on a single shared expert for all tasks, making it hard to produce reliable results. Meanwhile, the intrinsic redundancy and homogeneity of RS images bring substantial difficulties for both training and inference. These challenges directly conflict with the demands of remote sensing, which values task precision and trustworthy reasoning.To address these limitations, we propose GeoCoT, a manifold-driven mixture-of-experts (MoE) system with Chain-of-Thought (CoT) reasoning. GeoCoT introduces Mani-MoE, a sparse expert architecture grounded in local manifold mapping. It projects high-dimensional tokens onto low-rank subspaces adaptively to eliminate redundancy and uncover intrinsic structure, and then routes them through a sparse expert pathway, where gating decisions are guided by the manifold structure of the input.To optimize this architecture, we adopt a CoT-driven multi-stage training strategy. It leverages a cold-start phase for domain adaptation, followed by our RS Vision Group Relative Policy Optimization (RSV-GRPO) to systematically strengthen structured reasoning from global to objectives. Furthermore, we innovatively build *RS-CoT-20k* dataset for task-specific supervision.Extensive experiments on multi-task datasets demonstrate that GeoCoT outperforms prior approaches, achieving 5.27 \% higher average accuracy than the state-of-the-art method. Our code will be available.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:3d5668cb9f1de06951c9047b31a3c650CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Interpretable Cross-Domain Few-Shot Learning with Rectified Target-Domain Local Alignment

Zhao, Yaze, Zou, Yixiong, Li, Yuhua, Li, Ruixuan

原文摘要

Cross-Domain Few-Shot Learning (CDFSL) adapts models trained with large-scale general data (source domain) to downstream target domains with only scarce training data, where the research on vision-language models (e.g., CLIP) is still in the early stages. Typical downstream domains, such as medical diagnosis, require fine-grained visual cues for interpretable recognition, but we find that current fine-tuned CLIP models can hardly focus on these cues, albeit they can roughly focus on important regions in source domains. Although current works have demonstrated CLIP's shortcomings in capturing local subtle patterns, in this paper, we find that **the domain gap and scarce training data further exacerbate such shortcomings, much more than that of holistic patterns**, which we call the local misalignment problem in CLIP-based CDFSL. To address this problem, due to the lack of supervision in aligning local visual features and text semantics, we turn to self-supervision information. Inspired by the translation task, we propose the CC-CDFSL method with cycle consistency, which translates local visual features into text features and then translates them back into visual features (and vice versa), and constrains the original features close to the translated back features.To reduce the noise imported by richer information in the visual modality, we further propose a Semantic Anchor mechanism, which first augments visual features to provide a larger corpus for the text-to-image mapping, and then shrinks the image features to filter out irrelevant image-to-text mapping. Extensive experiments on various benchmarks, backbones, and fine-tuning methods show we can (1) effectively improve the local vision-language alignment, (2) enhance the interpretability of learned patterns and model decisions by visualizing patches, and (3) achieve state-of-the-art performance. Our codes will be released.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:4d607b85ff7ea4220a19f9a957c0e595CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Gastric-X: A Multimodal Multi-Phase Benchmark Dataset for Advancing Vision-Language Models in Gastric Cancer Analysis

Li, Yuanzhe, Chen, Hao, Yin, Rui, Ba, Juyan, Zhang, Yu, Lu, Sheng

原文摘要

Recent vision-language models (VLMs) have shown strong generalization and multimodal reasoning abilities in natural domains. However, their application to medical diagnosis remains limited by the lack of comprehensive and structured datasets that capture real clinical workflows. To advance the development of VLMs for clinical applications, particularly in gastric cancer, we introduce Gastric-X, a large-scale multimodal benchmark for gastric cancer analysis providing 1.7K cases. Each case in Gastric-X includes paired resting and dynamic CT scans, endoscopic image, a set of structured biochemical indicators, expert-authored diagnostic notes, and bounding box annotations of tumor regions, reflecting realistic clinical conditions. We systematically examine the capability of recent VLMs on five core tasks: Visual Question Answering (VQA), report generation, cross-modal retrieval, disease classification, and lesion localization. These tasks simulate critical stages of clinical workflow, from visual understanding and reasoning to multimodal decision support. Through this evaluation, we aim not only to assess model performance but also to probe the nature of VLM understanding: Can current VLMs meaningfully correlate biochemical signals with spatial tumor features and textual reports? We envision Gastric-X as a step toward aligning machine intelligence with the cognitive and evidential reasoning processes of physicians, and as a resource to inspire the development of next-generation medical VLMs.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:504d31e5684b54393db6015bd5460d4cCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

AREA3D: Active Reconstruction Agent with Unified Feed-Forward 3D Perception and Vision-Language Guidance

Xu, Tianling, Gan, Shengzhe, Gu, Leslie, Li, Yuelei, Zhan, Fangneng, Pfister, Hanspeter

原文摘要

Active 3D reconstruction enables an agent to autonomously select viewpoints to build accurate and complete scene geometry efficiently, rather than passively reconstructing scenes from pre-collected images. Existing active reconstruction methods often rely on geometric heuristics, which may result in redundant observations without improving reconstruction quality. To address this, we propose AREA3D, an active reconstruction agent for 3D reconstruction by leveraging feed-forward 3D models and vision-language guidance. The framework decouples view uncertainty modeling from feed-forward reconstruction, enabling precise uncertainty estimation without online optimization. Moreover, the integrated Vision-Language Model provides high-level semantic guidance that guides exploration beyond purely geometric cues. Extensive experiments on both scene-level and object-level benchmarks (Replica and OmniObject3D) demonstrate that AREA3D achieves state-of-the-art reconstruction accuracy, especially in sparse views.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:6049c4888cca5ea9d2bf395be307a7b5CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Addressing Exacerbated Attention Sink for Source-Free Cross-Domain Few-Shot Learning

Yi, Shuai, Zou, Yixiong, Li, Yuhua, Li, Ruixuan

原文摘要

Vision-language models (VLMs) like CLIP have shown impressive generalization capabilities, yet their potential for Cross-Domain Few-Shot Learning (CDFSL) remains underexplored, where the model needs to transfer source-domain information to target domains with scarce training data. While the attention sink phenomenon has been observed in VLMs for certain tasks, its role in CDFSL scenarios has not been studied. In this paper, we uncover a critical issue overlooked by prior works: standard target-domain few-shot fine-tuning in CDFSL significantly exacerbates the attention sink problem, leading to poor discriminability across classes. To understand this phenomenon, through extensive experiments, we interpret it as the model's shortcut learning for domain adaptation: to overcome the huge domain gap between the source and target domains, the model shows a high tendency to push tokens that are initially closer to target-domain classes (i.e., simple tokens) to be even closer to these classes, exacerbating the attention sink and wasting the capability of learning other discriminative but initially further tokens (i.e., hard tokens). To address this, we propose a novel approach to dynamically re-weight tokens according to their relevance with target-domain classes during the target-domain finetuning, which explicitly suppresses the model's reliance on these simple tokens and enhances the learning of hard tokens, reducing sink tokens and enhancing discriminability. Extensive experiments on four benchmark datasets validate the rationale of our method, demonstrating new state-of-the-art performance. Our codes are available at https://github.com/shuaiyi308/TIR.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:6a178e621ca6da84e1ecdfd88de50fa4CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

UNI-OOD: Unified Object- and Image-level Out-of-Distribution Detection via Cross-Context Attentive Vision-Language Modeling

Li, Yuchuan, Motamedi, Azadeh, Kwon, Hyock Ju, Park, Chul B, Kim, Il-Min

原文摘要

Out-of-distribution (OOD) detection is a key requirement for reliable deployment in open-world environments, where a model must recognize inputs that fall outside the semantic scope of known concepts. While recent advances in vision-language models (VLMs) have achieved strong results in image-level OOD detection, most methods still assume that each image contains a single dominant object. This assumption severely limits their applicability to real-world settings where scenes are naturally composed of multiple objects that each demand independent OOD assessment. Existing object-level approaches, including the current SOTA method RUNA, remain constrained by coarse global representations and insufficient modeling of contextual dependencies between objects and their backgrounds. We propose UNI-OOD, a unified framework that performs both object- and image-level OOD detection within a single VLM, without requiring prior knowledge of which task is being addressed at inference time. The key idea is to leverage cross-context attentive modeling that captures complementary visual and textual semantics. UNI-OOD learns to attend to fine-grained spatial details within each object, aligns visual and linguistic embeddings to strengthen semantic correspondence, and models interactions between target objects and their surrounding context. By jointly reasoning over object-centric and background cues, the framework disentangles informative visual evidence from spurious correlations and enables a consistent OOD scoring mechanism across different visual granularities. Extensive experiments on object- and image-level benchmarks demonstrate that UNI-OOD achieves substantial and consistent improvements over previous approaches, establishing new SOTA performance in both object-level and image-level OOD detection.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:6ae3941279150d112c3984bcc7beda51CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Vocabulary Scaling Law: Tuning Open-vocabulary Predictors for Their Openness

Chen, Ziliang, Li, Yulu, Fang, Liangda, Zhang, Jusheng, Zheng, Yongsen, Guan, Quanlong, Chen, Xipeng

原文摘要

Open-vocabulary learning on CLIP provides remarkable generalization on diverse concepts, however, falters under the realistic streaming open-world evaluations for Stability against distractor classes and Extensibility to novel classes. Current fine-tuning methods often fail these tests since they are mainly designed for closed-set conditions, leading to the performance gaps while the target vocabulary progressively scales. We formalize a "vocabulary scaling law" showing that these openness measures can be lower-bounded by performance on the full class-name universe, implying that robust fine-tuning should: (i) account for the entire vocabulary, (ii) tune class-name embeddings rather than context, and (iii) enforce orthogonality between prompt embeddings including training and open-set class names. Guided by our analysis, we propose Submodular-Vocabulary Fine-tuning (SVFT), a bi-level optimization framework that approximates the intractable objective of tuning all class name embedding by greedily selecting a small, informative subset of class names via constrained submodular maximization, thus, allows the employment of efficient greedy algorithm for the near-optimal class-name subset selection to fine-tune CLIP instead of using all open classes. Across extensive experiments, SVFT consistently improves both stability and extensibility, advancing the openness and practical robustness of CLIP-based vision-language models.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:85cff517508528ad6494f4498434d3fdCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

See What I Mean: Aligning Vision and Language Representations for Video Fine-grained Object Understanding

Sun, Boyuan, Yin, Bo-Wen, Li, Yuan-Ming, Wei, Xihan, Hou, Qibin

原文摘要

We present SWIM (See What I Mean), a novel training strategy that aligns vision and language representations to enable fine-grained object understanding solely from textual prompts. Unlike existing approaches that require explicit visual prompts, such as masks or points, SWIM leverages mask supervision only during training to guide cross-modal attention, allowing the model to automatically attend to the user-specified object at inference.Our cross-attention analysis of pretrained multimodal large language models (MLLMs) reveals a systematic discrepancy: Attribute words produce sharp, localized activations in the visual modality, whereas object nouns yield diffuse and scattered patterns due to semantic reference bias and distributed high-level representations. To address this misalignment, we construct NL-Refer, an enriched dataset, in which each object mask is paired with a precise natural language referring expression. SWIM extracts multi-layer cross-attention maps from object nouns and enforces spatial consistency with ground-truth masks. Experimental results demonstrate that SWIM substantially improves text-visual alignment and achieves superior performance over visual-prompt-based methods on fine-grained object understanding benchmarks. The code and data are available at https://github.com/HumanMLLM/SWIM.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:8b3322f6b6af90f9eff1f907e7250780CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

CF-IPT: Cross-Modal Fusion Interactive Prompt Tuning of Vision-Language Pre-Trained Model for Multisource Remote Sensing Data Classification

Ji, Jinheng, Qu, Jiahui, Dong, Wenqian, Li, Yunsong

原文摘要

Fine-tuning Vision-Language Models (VLMs) trained on large-scale datasets of natural image-text pairs has demonstrated impressive performance for various downstream tasks. However, their fine-tuning for remote sensing (RS) tasks faces dual barriers: (1) Data-level barrier caused by the fundamental modality gap between natural imagery and RS data, and (2) Task-level barrier stemming from the requirement for multi-source interaction modeling capabilities. This paper proposes a Cross-modal Fusion Interactive Prompt Tuning (CF-IPT) method to fine-tune CLIP for multi-source RS image classification tasks. It aims to leverage the prompt learning framework to transfer the alignment target of the text branch shifts from natural images to multi-source RS images. Specifically, we design a Multi-source Interactive Fusion-guided Spectral-Spatial Prompt Generation (MFPG) module, which enables cross-modal feature interaction to generate a prompt matrix that preserves the original spectral and spatial information while performing adaptive multi-scale fusion to address the multi-source image adaptation problem. Subsequently, a Spectral-Spatial Prompt-guided Visual-Text Prompt Interaction (V-TPI) Strategy is proposed, which leverages spectral-spatial prompt matrices to guide visual-textual prompt interaction and inject RS-specific information into both branches of CLIP, ultimately enabling multi-source RS image-text representation alignment. The proposed approach performs the downstream task of multi-source RS image classification with merely 0.76% of CLIP's parameters. It is evaluated on several widely used datasets, demonstrating the effectiveness of the proposed approach.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:9f2a7395b8b9b31c2853bc61aafe5678CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Mind the Discriminability Trap in Source-Free Cross-domain Few-shot Learning

Zhang, Zhenyu, Zou, Yixiong, Li, Yuhua, Li, Ruixuan, Chen, Guangyao

原文摘要

Source-Free Cross-Domain Few-Shot Learning (SF-CDFSL) focuses on fine-tuning with limited training data from target domains (e.g., medical or satellite images), where Vision-Language Models (VLMs) such as CLIP and SigLIP have shown promising results. Current works in traditional visual models suggest that improving visual discriminability enhances performance. However, in VLM-based SF-CDFSL tasks, we find that strengthening visual-modal discriminability actually suppresses VLMs' performance. In this paper, we aim to delve into this phenomenon for an interpretation and a solution.By both theoretical and experimental proofs, our study reveals that fine-tuning with the typical cross-entropy loss (L_ vlm ) inherently includes a visual learning part and a cross-modal learning part, where the cross-modal part is crucial for rectifying the heavily disrupted modality misalignment in SF-CDFSL.However, we find that the visual learning essentially acts as a shortcut that encourages the model to reduce L_ vlm without considering the cross-modal part, therefore hindering the cross-modal alignment and harming the performance.Based on this interpretation, we further propose an approach to address this problem: first, we perturb the visual learning to guide the model to focus on the cross-modal alignment. Then, we use the visual-text semantic relationships to gradually align the visual and textual modalities during the fine-tuning. Extensive experiments on various settings, backbones (CLIP, SigLip, PE-Core), and tasks (4 CDFSL datasets and 11 FSL datasets) show that we consistently set new state-of-the-art results. We will release the code.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:ba9fddf93861ad90354eb983ead1b01aCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding

Li, Yue, Ma, Qi, Yang, Runyi, Ma, Mengjiao, Ren, Bin, Popovic, Nikola, Sebe, Nicu, Gevers, Theo 等 11 位作者

原文摘要

While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-explored. We address this gap by introducing Chorus, a multi-teacher pretraining framework that learns a holistic feed-forward 3D Gaussian Splatting (3DGS) scene encoder by distilling complementary signals from 2D foundation models. Chorus employs a shared 3D encoder and teacher-specific projectors to learn from language-aligned, generalist, and object-aware teachers, encouraging a shared embedding space that captures signals from high-level semantics to fine-grained structure. We evaluate Chorus on a wide range of tasks: open-vocabulary semantic and instance segmentation, linear and decoder probing, data-efficient supervision, as well as LLM-based Q&A. Besides 3DGS, we also test Chorus on several benchmarks that only support point clouds by pretraining a variant using only Gaussians' centers, colors, estimated normals. Interestingly, this encoder shows strong transfer and outperforms the point clouds baseline while using 39.9xfewer training scenes. Finally, we propose a render-and-distill adaptation that facilitates out-of-domain finetuning. Our code and model is released at this codebase.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:d8190bb623a25be8037d3515535190a7CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

MotionMaster: Generalizable Text-Driven Motion Generation and Editing

Jiang, Nan, Li, Yunhao, Pang, Lexi, He, Zimo, Huang, Siyuan, Zhu, Yixin

原文摘要

Synthesizing realistic human motion from natural language holds transformative potential for animation, robotics, and virtual reality. Recent methods handle single-action sequences and simple textual instructions, yet multi-action compositions and precise editing remain elusive due to limited data diversity, inadequate representations, and fragmented pipelines. Critically, most existing methods train motion generation models from scratch, failing to exploithe rich action semantics and long-horizon reasoning already encoded in pretrained Multimodal Large Language Models (MLLMs). Here we show that finetuning a pretrained MLLM with large-scale motion data yields strong zero-shot generalization across diverse text-guided motion generation and editing tasks. We present MotionMaster, a unified framework built on three components: MotionGB, a 10,000-hour dataset expanded from 400 hours of verified motion capture via spatial-temporal augmentation; an FSQ-based tokenizer that preserves both local joint accuracy and global trajectory coherence; and a finetuned MLLM with motion and language tokens in a shared embedding space. MotionMaster outperforms prior methods by 41.6% in multi-action semantic consistency and 20.8% in body-part composition. These results demonstrate that pretraining knowledge from MLLMs transfers effectively to motion understanding, opening a viable path toward general-purpose motion intelligence.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:eaac07ce30ad7b947eb305dd6bc9f3dcCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

HulluEdit: Single-Pass Evidence-Consistent Subspace Editing for Mitigating Hallucinations in Large Vision-Language Models

Lin, Yangguang, Fang, Quan, Li, Yufei, Sun, Jiachen, Gao, Junyu, Sang, Jitao

原文摘要

Object hallucination in Large Vision-Language Models (LVLMs) significantly hinders their reliable deployment. Existing methods struggle to balance efficiency and accuracy: they often require expensive reference models and multiple forward passes, or apply static edits that risk suppressing genuine visual evidence. To address this, we introduce HulluEdit, a single-pass, reference-free intervention framework. Our core innovation is orthogonal subspace editing: we decompose the hidden states of the model into orthogonal subspaces--visual evidence, conflicting priors, and residual uncertainty--enabling selective suppression of hallucinatory patterns without interfering with visual grounding. This approach mathematically guarantees that edits applied to the prior subspace leave the visual component entirely unaffected. Extensive experiments show that HulluEdit achieves state-of-the-art hallucination reduction on benchmarks including POPE and CHAIR across diverse architectures, while preserving general capabilities on MME and maintaining efficient inference. Our method consistently outperforms contrastive decoding and static subspace editing baselines, offering a new pathway toward more trustworthy LVLMs. Code is released at https://github.com/VioAgnes/HulluEdit.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:fc20e0981dd18c7a27efd4320d4f048aCVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Towards Unified Human Perception and Machine Understanding: Token Flow Guided Compression Framework

Xu, Li, Zhang, Yingfu, Xu, Kepeng, He, Gang, Li, Yunsong

原文摘要

With the rapid rise of Large Vision Language Models (LVLMs) for image understanding, the objective of image compression is gradually shifting from human visual perception to machine-oriented semantic understanding. However, conventional learned compression techniques are optimized for pixel-level fidelity and typically operate at fixed or rigid bitrate points, misaligned with the semantic consistency and flexible bitrate control. This gap becomes critical in ultra-low-bitrate regimes, where latent representations often ignore semantic relevance and struggle to disentangle meaningful content from redundant visual details as the bitrate varies. To address these challenges, we develop a token-based flexible compression framework, Token Flow Guided Compression (TFGC), which unifies human- and machine-oriented objectives. TFGC supports variable bitrate control in ultra-low bitrate regimes and enables LVLMs to directly process compressed token without image reconstruction. Specifically, we explore token flow phenomenon in 1D token sequences and exploit it to design token flow propagation, which predicts missing tokens by propagating contextual information from unmasked tokens. Moreover, token semantic guidance aligns compressed representations with LVLM semantic space, while a progressive semantic alignment training strategy further bridges the gap between perceptual reconstruction and semantic reasoning. Experiments show that our framework achieves state-of-the-art LVLMs understanding at comparable bitrates while maintaining satisfactory perceptual quality.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗

首次观测 · 记录 #1CVF · CVPR 2026:cvf:ff8d0ffa3a5b230c68d1e2efeb031c68CVF · CVPR 2026

来源日期 2026-01-01（仅到年份）

Multi-Paradigm Collaborative Adversarial Attack Against Multi-Modal Large Language Models

Li, Yuanbo, Xu, Tianyang, Hu, Cong, Zhou, Tao, Wu, Xiao-Jun, Kittler, Josef

原文摘要

The rapid progress of Multi-Modal Large Language Models (MLLMs) has significantly advanced downstream applications. However, this progress also exposes serious transferable adversarial vulnerabilities. In general, existing adversarial attacks against MLLMs typically rely on surrogate models trained within a single learning paradigm and perform independent optimisation in their respective feature spaces. This straightforward setting naturally restricts the richness of feature representations, delivering limits on the search space and thus impeding the diversity of adversarial perturbations. To address this, we propose a novel Multi-Paradigm Collaborative Attack (MPCAttack) framework to boost the transferability of adversarial examples against MLLMs. In principle, MPCAttack aggregates semantic representations, from both visual images and language texts, to facilitate joint adversarial optimisation on the aggregated features through a Multi-Paradigm Collaborative Optimisation (MPCO) strategy. By performing contrastive matching on multi-paradigm features, MPCO adaptively balances the importance of different paradigm representations and guides the global perturbation optimisation, effectively alleviating the representation bias. Extensive experimental results on multiple benchmarks demonstrate the superiority of MPCAttack, indicating that our solution consistently outperforms state-of-the-art methods in both targeted and untargeted attacks on open-source and closed-source MLLMs. The code is released at https://github.com/LiYuanBoJNU/MPCAttack.

cs.CV · cs.CL

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

CVF · CVPR 2026 ↗PDF ↗