Roadside perception datasets are typically constructed via cooperative labeling between synchronized vehicle and roadside frame pairs, but real deployment is often limited roadside-only data due to hardware and privacy constraints. The observation that even human experts struggle to produce accurate labels without vehicle-side data reveals a fundamental learnability problem: many roadside-only scenes contain distant, blurred, or occluded objects whose 3D properties are ambiguous from a single view and can only be reliably annotated by cross-checking paired vehicle--roadside frames. We refer to such cases as inherently ambiguous samples. In this work, we develop an active learning framework for roadside monocular 3D object detection and propose a learnability-driven framework that selects scenes which are both informative and reliably labelable, suppressing inherently ambiguous samples while ensuring coverage. Experiments demonstrate that our method significantly outperforms uncertainty-based baselines, which suggests that learnability, not uncertainty, matters for roadside 3D perception.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Prompt Learning (PL) has emerged as a parameter-efficient technique for adapting Vision-Language Models (VLMs) to downstream tasks. However, almost all existing PL methods are primarily designed and evaluated on well-curated datasets, overlooking a critical post-deployment phenomenon, i.e., the intrinsic connection between input resolution and storage-memory consumption. Specifically, to satisfy the stringent storage-memory constraints on edge devices, models are often limited to low-resolution inputs (e.g., <224x224 for CLIP-ViT/B-16) and generate fewer tokens (with the position embedding resized), which poses a unique challenge in performance robustness. To tackle this issue, we propose LOREAL, an efficient prompt self-distillation framework that learns resolution-invariant representations by excavating attribute semantics. At the heart of LOREAL is a dual-student architecture, i.e., two student models fed with inputs at different resolutions synergistically learn from each other. Building upon this, we contextualize the students' prompt with resolution-invariant attributes queried from the LLM, then leverage cross-modality meta-nets to generate attribute semantics. These meta-nets are bridged between the different encoders of two students, wherein we introduce Low-Level Distillation (LLD) and High-Level Distillation (HLD) to facilitate the learning of more cross-resolution representations. Extensive experiments show that LOREAL significantly improves VLMs' performance and robustness under varied resolution settings, underscoring significant practical utilities.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Self-attention with separate pre- and post-projections can be a universal approximator (on compact domains) under mild conditions. Yet we observe a striking gap: an attention-only Transformer (w/o FFN layers) exhibits a marked accuracy drop relative to its standard interleaved attention--FFN baseline. We term this the weak-independence challenge of attention. We study this through a new conceptual lens, Selection-as-Nonlinearity (SaN), which interprets effective nonlinearity as directed, cost-constrained selection, offering a coherent account of attention as context-gated activation. In this joint game-decision view, attention performs a resource-constrained cooperative allocation over values: each query distributes a unit-mass weight budget over shared values to optimize representational utility, under a normalizer (e.g., \mathrm softmax ), and guided by context-derived scores (e.g., q-k similarities). SaN interprets weak-independence as a structural tension: the value weights almost cannot simultaneously attain the decoupled per-query (row-wise) and the per-value (column-wise) optimums under shared budgets, thereby limiting attention's stand-alone capacity. Guided by SaN, we introduce CSaN, an interpretable, efficient attention compensation paradigm with two key insights: 1) hierarchical budget calibration, re-allocate row budgets via inter-query correction signals; and 2) public-private cooperation, enhancing the public attention pathway with a per-token private value pathway to decouple conflicting demands. CSaN is evaluated on various vision benchmarks and demonstrates remarkable gains across popular Transformer families (Swin, ViT, Hiera), enabling models to rival much heavier same-family counterparts ~2xas large.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
The advent of multimodal agents facilitates effective interaction within graphical user interface (GUI), especially in ubiquitous GUI control. However, their inability to reliably execute toggle control instructions remains a key bottleneck. To investigate this, we construct a state control benchmark with binary toggle instructions derived from public datasets. Evaluation results of existing agents demonstrate their notable unreliability, particularly when the current toggle state already matches the desired state. To address the challenge, we propose State-aware Reasoning (StaR), a multimodal reasoning method that enables agents to perceive the current toggle state, infer the desired state from the instruction, and act accordingly. Experiments on four multimodal agents demonstrate that StaR can improve toggle instruction execution accuracy by over 30%. Further evaluations on three public agentic benchmarks show that StaR also enhances general agentic task performance. Finally, evaluations on a dynamic environment highlight the potential of StaR for real-world applications. Code and benchmark: https://github.com/ZrW00/StaR.
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cs.CV · cs.AI
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表