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.
尚无 AI 解读，展示原文摘要
cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Ji, Jinheng, Qu, Jiahui, Dong, Wenqian, Li, Yunsong
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.
尚无 AI 解读，展示原文摘要
cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Vision Transformers (ViTs) exhibit robust performance across diverse visual scenarios. However, their efficiency is constrained by excessive token counts. Token merging offers a viable solution for achieving efficient ViTs. Existing methods merge tokens based solely on specific characteristics within the attention mechanism, which changes significantly across layers. In this paper, we propose a novel training-free SAliency-Driven Token Merging (SAD-TM) approach by leveraging not only the semantic relevance in the attention space but also the latent visual saliency of input patches. Our SAD-TM is inspired by the discovery that saliency-based statistics can directly capture the causal relationship between model input and output, regardless of the layers. Based on the observation, we develop a method that is mathematically formulated to merge tokens with high saliency outliers. The principle behind our merging is that tokens with high saliency outliers usually imply inconsistencies with the global gradient direction, and thus can be merged safely. Besides, our systematic analysis indicates that class attention shows considerable variation across early blocks, so a deferred merging strategy is introduced to optimize the selection of merging rates. In a training-free manner, SAD-TM demonstrates superior performance across various ViT architectures. Especially, with a FLOPs compression of 23.08% on DeiT-Tiny, SAD-TM achieves a Top-1 Accuracy comparable to the pretrained baseline on ImageNet dataset. The code will be available soon.
尚无 AI 解读，展示原文摘要
cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
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.
尚无 AI 解读，展示原文摘要
cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表