COLLAR: Cascaded Object-Level Latent Refinement for High-Fidelity Conditional Generation

• URL: http://arxiv.org/abs/2606.00954v1
• Date: Sun, 31 May 2026 02:10:34 GMT
• Title: COLLAR: Cascaded Object-Level Latent Refinement for High-Fidelity Conditional Generation
• Abstract summary: We propose a training-free framework that progressively optimize object-level features via the Field-of-View (FoV) expansion.<n>Our approach consistently outperforms state-of-the-art methods across semantic alignment, image quality, and spatial fidelity.
• Score: 8.166232715151436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving high-fidelity object-level control in Diffusion Transformers remains a significant challenge despite the introduction of structural priors like depth and Canny maps. Current object-level conditional generation methods frequently suffer from visual artifacts and struggle to maintain precise control over objects within small localized regions. To address these limitations, we propose Cascaded Object-Level Latent Refinement (COLLAR), a training-free framework that progressively optimizes object-level features via the Field-of-View (FoV) expansion. First, we propose the Cross-Scale Semantic Alignment (CSSA) module to address spatial-semantic gaps by injecting object-level features into extended-FoV branches via attention mechanisms. To further optimize these features, the Cyclic Feature Injection (CFI) module introduces a reciprocal background feedback mechanism. It leverages a frequency-based adaptive strategy to selectively update the global backbone with context-aligned local information. Finally, the extended-FoV branch serves as a hub for feature optimization, ensuring that object-level features are integrated into the global generation process without compromising final image quality. Extensive experiments on the COCO-MIG and COCO-POS benchmarks demonstrate that our approach consistently outperforms state-of-the-art methods across semantic alignment, image quality, and spatial fidelity.

Related papers

• Topology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification [11.354716471312239]
  Domain adaptation has advanced cross-scene hyperspectral image classification, significantly improving discriminative capability in complex scenarios.<n>Conventional domain adaptation methods become impractical, severely restricting their utility in realistic remote sensing scenarios.<n>We propose a topology-aware source-free learning framework to achieve robust and accurate classification without source data.
  arXiv  Detail & Related papers  (2026-08-06T12:40:06Z)
• Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization [56.95448807869383]
  Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning.<n>Recent studies find that the gradients exchanged in the FL system are also vulnerable to privacy leakage.<n>We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD)
  arXiv  Detail & Related papers  (2026-04-01T14:32:15Z)
• Small Object Detection in Complex Backgrounds with Multi-Scale Attention and Global Relation Modeling [8.24377869183113]
  Small object detection under complex backgrounds is a challenging task due to severe feature degradation, weak semantic representation, and inaccurate localization.<n>Existing detection frameworks are mainly designed for general objects.<n>We propose a multi-level feature enhancement and global relation modeling framework tailored for small object detection.
  arXiv  Detail & Related papers  (2026-03-04T06:57:46Z)
• IoUCert: Robustness Verification for Anchor-based Object Detectors [58.35703549470485]
  We introduce IoUCert, a novel formal verification framework designed specifically to overcome these bottlenecks in anchor-based object detection architectures.<n>We show that our method enables the robustness verification of realistic, anchor-based models including SSD, YOLOv2, and YOLOv3 variants against various input perturbations.
  arXiv  Detail & Related papers  (2026-03-03T14:36:46Z)
• StepVAR: Structure-Texture Guided Pruning for Visual Autoregressive Models [98.72926158261937]
  We propose a training-free token pruning framework for Visual AutoRegressive models.<n>We employ a lightweight high-pass filter to capture local texture details, while leveraging Principal Component Analysis (PCA) to preserve global structural information.<n>To maintain valid next-scale prediction under sparse tokens, we introduce a nearest neighbor feature propagation strategy.
  arXiv  Detail & Related papers  (2026-03-02T11:35:05Z)
• FMC-DETR: Frequency-Decoupled Multi-Domain Coordination for Aerial-View Object Detection [18.023418423273082]
  We propose FMC-DETR, a novel framework with frequency-decoupled fusion for aerial-view object detection.<n>First, we introduce the Wavelet Kolmogorov-Arnold Transformer (WeKat) backbone, which applies cascaded wavelet transforms to enhance global low-frequency context perception.<n>Next, a lightweight Cross-stage Partial Fusion (CPF) module reduces redundancy and improves multi-scale feature interaction.<n>Finally, we introduce the Multi-Domain Feature Coordination (MDFC) module, which unifies spatial, frequency, and structural priors to balance detail preservation and global enhancement.
  arXiv  Detail & Related papers  (2025-09-27T02:28:22Z)
• CEM-FBGTinyDet: Context-Enhanced Foreground Balance with Gradient Tuning for tiny Objects [2.321156185872456]
  We propose E-FPN-BS, a novel architecture integrating multi-scale feature enhancement and adaptive optimization.<n>First, our Context Enhancement Module(CEM) employs dual-branch processing to align and compress high-level features for effective global-local fusion.<n>Second, the Foreground-Background Separation Module (FBSM) generates spatial gating masks that dynamically amplify discriminative regions.
  arXiv  Detail & Related papers  (2025-06-11T16:13:38Z)
• VRS-UIE: Value-Driven Reordering Scanning for Underwater Image Enhancement [104.78586859995333]
  State Space Models (SSMs) have emerged as a promising backbone for vision tasks due to their linear complexity and global receptive field.<n>The predominance of large-portion, homogeneous but useless oceanic backgrounds can dilute the feature representation responses of sparse yet valuable targets.<n>We propose a novel Value-Driven Reordering Scanning framework for Underwater Image Enhancement (UIE)<n>Our framework sets a new state-of-the-art, delivering superior enhancement performance (surpassing WMamba by 0.89 dB on average) by effectively suppressing water bias and preserving structural and color fidelity.
  arXiv  Detail & Related papers  (2025-05-02T12:21:44Z)
• CFMD: Dynamic Cross-layer Feature Fusion for Salient Object Detection [7.262250906929891]
  Cross-layer feature pyramid networks (CFPNs) have achieved notable progress in multi-scale feature fusion and boundary detail preservation for salient object detection.<n>To address these challenges, we propose CFMD, a novel cross-layer feature pyramid network that introduces two key innovations.<n>First, we design a context-aware feature aggregation module (CFLMA), which incorporates the state-of-the-art Mamba architecture to construct a dynamic weight distribution mechanism.<n>Second, we introduce an adaptive dynamic upsampling unit (CFLMD) that preserves spatial details during resolution recovery.
  arXiv  Detail & Related papers  (2025-04-02T03:22:36Z)
• Centralized Feature Pyramid for Object Detection [53.501796194901964]
  Visual feature pyramid has shown its superiority in both effectiveness and efficiency in a wide range of applications. In this paper, we propose a OLO Feature Pyramid for object detection, which is based on a globally explicit centralized feature regulation.
  arXiv  Detail & Related papers  (2022-10-05T08:32:54Z)
• Global Context-Aware Progressive Aggregation Network for Salient Object Detection [117.943116761278]
  We propose a novel network named GCPANet to integrate low-level appearance features, high-level semantic features, and global context features. We show that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2020-03-02T04:26:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.