ORIC: Benchmarking Object Recognition in Incongruous Context for Large Vision-Language Models

Large Vision-Language Models (LVLMs) have made significant strides in image caption, visual question answering, and robotics by integrating visual and textual information. However, they remain prone to errors in incongruous contexts, where objects appear unexpectedly or are absent when contextually expected. This leads to two key recognition failures: object misidentification and hallucination. To systematically examine this issue, we introduce the Object Recognition in Incongruous Context Benchmark (ORIC), a novel benchmark that evaluates LVLMs in scenarios where object-context relationships deviate from expectations. ORIC employs two key strategies: (1) LLM-guided sampling, which identifies objects that are present but contextually incongruous, and (2) CLIP-guided sampling, which detects plausible yet nonexistent objects that are likely to be hallucinated, thereby creating an incongruous context. Evaluating 18 LVLMs and two open-vocabulary detection models, our results reveal significant recognition gaps, underscoring the challenges posed by contextual incongruity. This work provides critical insights into LVLMs' limitations and encourages further research on context-aware object recognition.

SegDINO3D: 3D Instance Segmentation Empowered by Both Image-Level and Object-Level 2D Features

In this paper, we present SegDINO3D, a novel Transformer encoder-decoder framework for 3D instance segmentation. As 3D training data is generally not as sufficient as 2D training images, SegDINO3D is designed to fully leverage 2D representation from a pre-trained 2D detection model, including both image-level and object-level features, for improving 3D representation. SegDINO3D takes both a point cloud and its associated 2D images as input. In the encoder stage, it first enriches each 3D point by retrieving 2D image features from its corresponding image views and then leverages a 3D encoder for 3D context fusion. In the decoder stage, it formulates 3D object queries as 3D anchor boxes and performs cross-attention from 3D queries to 2D object queries obtained from 2D images using the 2D detection model. These 2D object queries serve as a compact object-level representation of 2D images, effectively avoiding the challenge of keeping thousands of image feature maps in the memory while faithfully preserving the knowledge of the pre-trained 2D model. The introducing of 3D box queries also enables the model to modulate cross-attention using the predicted boxes for more precise querying. SegDINO3D achieves the state-of-the-art performance on the ScanNetV2 and ScanNet200 3D instance segmentation benchmarks. Notably, on the challenging ScanNet200 dataset, SegDINO3D significantly outperforms prior methods by +8.7 and +6.8 mAP on the validation and hidden test sets, respectively, demonstrating its superiority.

Cumulative Consensus Score: Label-Free and Model-Agnostic Evaluation of Object Detectors in Deployment

Evaluating object detection models in deployment is challenging because ground-truth annotations are rarely available. We introduce the Cumulative Consensus Score (CCS), a label-free metric that enables continuous monitoring and comparison of detectors in real-world settings. CCS applies test-time data augmentation to each image, collects predicted bounding boxes across augmented views, and computes overlaps using Intersection over Union. Maximum overlaps are normalized and averaged across augmentation pairs, yielding a measure of spatial consistency that serves as a proxy for reliability without annotations. In controlled experiments on Open Images and KITTI, CCS achieved over 90% congruence with F1-score, Probabilistic Detection Quality, and Optimal Correction Cost. The method is model-agnostic, working across single-stage and two-stage detectors, and operates at the case level to highlight under-performing scenarios. Altogether, CCS provides a robust foundation for DevOps-style monitoring of object detectors.

RT-DETR++ for UAV Object Detection

Object detection in unmanned aerial vehicle (UAV) imagery presents significant challenges. Issues such as densely packed small objects, scale variations, and occlusion are commonplace. This paper introduces RT-DETR++, which enhances the encoder component of the RT-DETR model. Our improvements focus on two key aspects. First, we introduce a channel-gated attention-based upsampling/downsampling (AU/AD) mechanism. This dual-path system minimizes errors and preserves details during feature layer propagation. Second, we incorporate CSP-PAC during feature fusion. This technique employs parallel hollow convolutions to process local and contextual information within the same layer, facilitating the integration of multi-scale features. Evaluation demonstrates that our novel neck design achieves superior performance in detecting small and densely packed objects. The model maintains sufficient speed for real-time detection without increasing computational complexity. This study provides an effective approach for feature encoding design in real-time detection systems.

InsFusion: Rethink Instance-level LiDAR-Camera Fusion for 3D Object Detection

Three-dimensional Object Detection from multi-view cameras and LiDAR is a crucial component for autonomous driving and smart transportation. However, in the process of basic feature extraction, perspective transformation, and feature fusion, noise and error will gradually accumulate. To address this issue, we propose InsFusion, which can extract proposals from both raw and fused features and utilizes these proposals to query the raw features, thereby mitigating the impact of accumulated errors. Additionally, by incorporating attention mechanisms applied to the raw features, it thereby mitigates the impact of accumulated errors. Experiments on the nuScenes dataset demonstrate that InsFusion is compatible with various advanced baseline methods and delivers new state-of-the-art performance for 3D object detection.

RAVE: Retrieval and Scoring Aware Verifiable Claim Detection

The rapid spread of misinformation on social media underscores the need for scalable fact-checking tools. A key step is claim detection, which identifies statements that can be objectively verified. Prior approaches often rely on linguistic cues or claim check-worthiness, but these struggle with vague political discourse and diverse formats such as tweets. We present RAVE (Retrieval and Scoring Aware Verifiable Claim Detection), a framework that combines evidence retrieval with structured signals of relevance and source credibility. Experiments on CT22-test and PoliClaim-test show that RAVE consistently outperforms text-only and retrieval-based baselines in both accuracy and F1.

Dark-ISP: Enhancing RAW Image Processing for Low-Light Object Detection

Low-light Object detection is crucial for many real-world applications but remains challenging due to degraded image quality. While recent studies have shown that RAW images offer superior potential over RGB images, existing approaches either use RAW-RGB images with information loss or employ complex frameworks. To address these, we propose a lightweight and self-adaptive Image Signal Processing (ISP) plugin, Dark-ISP, which directly processes Bayer RAW images in dark environments, enabling seamless end-to-end training for object detection. Our key innovations are: (1) We deconstruct conventional ISP pipelines into sequential linear (sensor calibration) and nonlinear (tone mapping) sub-modules, recasting them as differentiable components optimized through task-driven losses. Each module is equipped with content-aware adaptability and physics-informed priors, enabling automatic RAW-to-RGB conversion aligned with detection objectives. (2) By exploiting the ISP pipeline's intrinsic cascade structure, we devise a Self-Boost mechanism that facilitates cooperation between sub-modules. Through extensive experiments on three RAW image datasets, we demonstrate that our method outperforms state-of-the-art RGB- and RAW-based detection approaches, achieving superior results with minimal parameters in challenging low-light environments.

MSGFusion: Multimodal Scene Graph-Guided Infrared and Visible Image Fusion

Infrared and visible image fusion has garnered considerable attention owing to the strong complementarity of these two modalities in complex, harsh environments. While deep learning-based fusion methods have made remarkable advances in feature extraction, alignment, fusion, and reconstruction, they still depend largely on low-level visual cues, such as texture and contrast, and struggle to capture the high-level semantic information embedded in images. Recent attempts to incorporate text as a source of semantic guidance have relied on unstructured descriptions that neither explicitly model entities, attributes, and relationships nor provide spatial localization, thereby limiting fine-grained fusion performance. To overcome these challenges, we introduce MSGFusion, a multimodal scene graph-guided fusion framework for infrared and visible imagery. By deeply coupling structured scene graphs derived from text and vision, MSGFusion explicitly represents entities, attributes, and spatial relations, and then synchronously refines high-level semantics and low-level details through successive modules for scene graph representation, hierarchical aggregation, and graph-driven fusion. Extensive experiments on multiple public benchmarks show that MSGFusion significantly outperforms state-of-the-art approaches, particularly in detail preservation and structural clarity, and delivers superior semantic consistency and generalizability in downstream tasks such as low-light object detection, semantic segmentation, and medical image fusion.

A Synthetic Data Pipeline for Supporting Manufacturing SMEs in Visual Assembly Control

Quality control of assembly processes is essential in manufacturing to ensure not only the quality of individual components but also their proper integration into the final product. To assist in this matter, automated assembly control using computer vision methods has been widely implemented. However, the costs associated with image acquisition, annotation, and training of computer vision algorithms pose challenges for integration, especially for small- and medium-sized enterprises (SMEs), which often lack the resources for extensive training, data collection, and manual image annotation. Synthetic data offers the potential to reduce manual data collection and labeling. Nevertheless, its practical application in the context of assembly quality remains limited. In this work, we present a novel approach for easily integrable and data-efficient visual assembly control. Our approach leverages simulated scene generation based on computer-aided design (CAD) data and object detection algorithms. The results demonstrate a time-saving pipeline for generating image data in manufacturing environments, achieving a mean Average Precision (mAP@0.5:0.95) up to 99,5% for correctly identifying instances of synthetic planetary gear system components within our simulated training data, and up to 93% when transferred to real-world camera-captured testing data. This research highlights the effectiveness of synthetic data generation within an adaptable pipeline and underscores its potential to support SMEs in implementing resource-efficient visual assembly control solutions.

Gradient-based multi-focus image fusion with focus-aware saliency enhancement

Multi-focus image fusion (MFIF) aims to yield an all-focused image from multiple partially focused inputs, which is crucial in applications cover sur-veillance, microscopy, and computational photography. However, existing methods struggle to preserve sharp focus-defocus boundaries, often resulting in blurred transitions and focused details loss. To solve this problem, we propose a MFIF method based on significant boundary enhancement, which generates high-quality fused boundaries while effectively detecting focus in-formation. Particularly, we propose a gradient-domain-based model that can obtain initial fusion results with complete boundaries and effectively pre-serve the boundary details. Additionally, we introduce Tenengrad gradient detection to extract salient features from both the source images and the ini-tial fused image, generating the corresponding saliency maps. For boundary refinement, we develop a focus metric based on gradient and complementary information, integrating the salient features with the complementary infor-mation across images to emphasize focused regions and produce a high-quality initial decision result. Extensive experiments on four public datasets demonstrate that our method consistently outperforms 12 state-of-the-art methods in both subjective and objective evaluations. We have realized codes in https://github.com/Lihyua/GICI