Autoregressive models have emerged as a powerful approach for visual generation but suffer from slow inference speed due to their sequential token-by-token prediction process. In this paper, we propose a simple yet effective approach for parallelized autoregressive visual generation that improves generation efficiency while preserving the advantages of autoregressive modeling. Our key insight is that parallel generation depends on visual token dependencies—tokens with weak dependencies can be generated in parallel, while strongly dependent adjacent tokens are difficult to generate together, as their independent sampling may lead to inconsistencies. Based on this observation, we develop a parallel generation strategy that generates distant tokens with weak dependencies in parallel while maintaining sequential generation for strongly dependent local tokens. Our approach can be seamlessly integrated into standard autoregressive models without modifying the architecture or tokenizer. Experiments on ImageNet and UCF-101 demonstrate that our method achieves a 3.6× speedup with comparable quality and up to 9.5× speedup with minimal quality degradation across both image and video generation tasks. We hope this work will inspire future research in efficient visual generation and unified autoregressive modeling. Project page: https://yuqingwang1029.github.io/PAR-project.

With the rapid advancement of the Industrial Internet of Things (IIoT), Digital Twin Networks (DTNs) have emerged as critical enablers for industrial network optimization. However, current full-space industrial DTNs face persistent challenges: neglect of propagation-impacting industrial factors, computational inefficiency, and poor generalization in Artificial Intelligence (AI)-based prediction method. This demo establishes an immersive IIoT-DTNs framework for real logistics scenarios, where cargo stacking rates are considered as the primary industrial dynamic factor. Hence, the 3D scenes reflecting varying stacking rates are generated for Ray Tracing (RT)-based channel modeling. The proposed Adaptive Sampling-based Sparse RT Calculation (AdaSpRT) method strategically selects critical propagation areas for RT calculation, replacing random sampling. Then, AI-based channel prediction can be trained and fine-tuned with the RT data for remaining sample prediction. Evaluations across two distinct environments, high-density shelving vs. open layout, demonstrate 22.0% and 12.4% efficiency improvements over conventional RT while maintaining Mean Absolute Errors (MAE) of 3.44 dB and 1.56 dB respectively.

With the rapid growth of video generative models (VGMs), it is essential to develop reliable and comprehensive automatic metrics for AI-generated videos (AIGVs). Existing methods either use off-the-shelf models optimized for other tasks or rely on human assessment data to train specialized evaluators. These approaches are constrained to specific evaluation aspects and are difficult to scale with the increasing demands for finer-grained and more comprehensive evaluations. To address this issue, this work investigates the feasibility of using multimodal large language models (MLLMs) as a unified evaluator for AIGVs, leveraging their strong visual perception and language understanding capabilities. To evaluate the performance of automatic metrics in unified AIGV evaluation, we introduce a benchmark called UVE-Bench. UVE-Bench collects videos generated by state-of-the-art VGMs and provides pairwise human preference annotations across 15 evaluation aspects. Using UVE-Bench, we extensively evaluate 18 MLLMs. Our empirical results suggest that while advanced MLLMs (e.g., Qwen2VL-72B and InternVL2.5-78B) still lag behind human evaluators, they demonstrate promising ability in unified AIGV evaluation, significantly surpassing existing specialized evaluation methods. Additionally, we conduct an in-depth analysis of key design choices that impact the performance of MLLM-driven evaluators, offering valuable insights for future research on AIGV evaluation.

Recent advances in Vision-Language Models (VLMs) and the scarcity of high-quality multimodal alignment data have inspired numerous researches on synthetic VLM data generation.The conventional norm in VLM data construction uses a mixture of specialists in caption and OCR, or stronger VLM APIs and expensive human annotation.In this paper, we present World to Code (W2C), a meticulously curated multi-modal data construction pipeline that organizes the final generation output into a Python code format.The pipeline leverages the VLM itself to extract cross-modal information via different prompts and filter the generated outputs again via a consistency filtering strategy.Experiments have demonstrated the high quality of W2C by improving various existing visual question answering and visual grounding benchmarks across different VLMs.Further analysis also demonstrates that the new code parsing ability of VLMs presents better crossmodal equivalence than the commonly used detail caption ability.Our code is available at https://github.com/foundation-multimodal- models/World2Code.

Large vision-language models (LVLMs) have recently achieved rapid progress, exhibiting great perception and reasoning abilities concerning visual information. However, when faced with prompts in different sizes of solution spaces, LVLMs fail to always give consistent answers regarding the same knowledge point. This inconsistency of answers between different solution spaces is prevalent in LVLMs and erodes trust. To this end, we provide a multi-modal benchmark ConBench, to intuitively analyze how LVLMs perform when the solution space of a prompt revolves around a knowledge point. Based on the ConBench tool, we are the first to reveal the tapestry and get the following findings: (1) In the discriminate realm, the larger the solution space of the prompt, the lower the accuracy of the answers. (2) Establish the relationship between the discriminative and generative realms: the accuracy of the discriminative question type exhibits a strong positive correlation with its Consistency with the caption. (3) Compared to open-source models, closed-source models exhibit a pronounced bias advantage in terms of Consistency. Eventually, we ameliorate the consistency of LVLMs by trigger-based diagnostic refinement, indirectly improving the performance of their caption. We hope this paper will accelerate the research community in better evaluating their models and encourage future advancements in the consistency domain.

Existing image-text modality alignment in Vision Language Models (VLMs) treats each text token equally in an autoregressive manner. Despite being simple and effective, this method results in sub-optimal cross-modal alignment by over-emphasizing the text tokens that are less correlated with or even contradictory with the input images. In this paper, we advocate for distinct contributions for each text token based on its visual correlation. Specifically, we present by contrasting image inputs, the difference in prediction logits on each text token provides strong guidance of visual correlation. We therefore introduce Contrastive Alignment (CAL), a simple yet effective re-weighting strategy that prioritizes training visually correlated tokens. Our experimental results demonstrate that CAL consistently improves different types of VLMs across different resolutions and model sizes on various benchmark datasets. Importantly, our method incurs minimal additional computational overhead, rendering it highly efficient compared to alternative data scaling strategies.

Recently, ship detection methods based on deep learning have attracted significant attention due to their superior accuracy over traditional methods. However, there still exist two problems affecting its robustness in practical application. 1) The size of ships in one image varies greatly, i.e., different sizes; 2) Numerous ships gather in limited field-of-view, i.e., dense distribution. To address these problems, we propose a rotational Libra R-convolutional neural network (CNN) method. Our idea is to balance the three levels of neural networks for predicting the location of ships with rotational angle information, which refers to the feature level, sample level, and objective level. First, to extract a discriminative feature and improve its robustness against the impact of different sizes of ships, the concept of balanced feature pyramid is introduced. Second, to generate reliable proposals for feature pyramid and efficiently mine hard negative samples, we employ intersection over union (IoU)-balanced sampling. Finally, to eliminate the redundant background and detect densely distributed ships, we bring in a rotational region detection branch with balanced L1 loss. In general, we develop the balanced learning with rotational region detection to achieve consistent improvement on accuracy and visualization. Experimental results on DOTA data set show that the proposed method achieves the state-of-the-art accuracy.

Security inspection aims to improve the high detection rate as well as reduce the false alarm rate. However, it still suffers from two challenges affecting its robustness. 1) Existing security inspection methods are mostly designed for natural images, which cannot reflect the uniqueness and imaging principle of THz images. 2) Existing methods is sensitive to noise interference and pose variations. This work revisits these challenges and presents a novel symmetry driven Siamese network (SDSN) for THz concealed object verification. Our idea is to employ a specially designed network architecture for THz concealed object verification. First, to reflect the uniqueness and the special property of THz images, Siamese network with Contrastive loss is used for feature extraction along with symmetrical prior information consideration, which can learn symmetrical metrics from the same person. Second, to alleviate the impact of noise interference and pose variations, the adaptive identity normalization (A-IDN) is proposed to normalize the symmetrical metrics each person. Finally, to enhance the generalization of network, an adaptive selective threshold based on Gaussian mixture model (AST-GMM) is designed, which serves as a classifier for the final classification results. Extensive experiments show that SDSN significantly improves the accuracy. Specially, SDSN outperforms the state-of-the-art methods without symmetrical prior information on THz security dataset.