Existing Text-to-Image (T2I) benchmarks remain anchored in foundational criteria such as image quality and text-image alignment, failing to capture the nuanced capabilities that professional creators demand in real-world workflows. Meanwhile, evaluation pipelines that rely on an untrained multimodal model as a judge diverge from human standards. We introduce Qwen-Image-Bench, a creator-centric benchmark that extends evaluation with two application-driven dimensions (Real-world Fidelity and Creative Generation), alongside the conventional pillars of Quality, Aesthetics, and Alignment. The benchmark organizes these into a three-level taxonomy (5 pillars, 23 sub-capabilities, 56 verifiable rubrics) and provides 1000 stratified bilingual prompts. We train a unified Judge Model (Q-Judger) on Qwen3.6-27B, supervised by 80 professional annotators under blind labeling and triple-review protocols, that scores every image across all 56 facets with fine-grained, rubric-grounded diagnostics. Empirically, Qwen-Image-Bench reliably distinguishes leading T2I models, achieving the greatest separation on the application-driven dimensions where existing benchmarks provide limited insight.

Recent advances in image generation and editing have been driven by powerful pretrained unified models, yet most existing approaches remain fundamentally raster-centric: they represent images as opaque pixels without natively modeling transparency. This limitation restricts their applicability to a broad range of real-world applications. In this work, we present Qwen-Image-Layered 1.5, a pretrained unified image generation and editing model with native transparency awareness. By jointly modeling visual appearance and transparency, our framework supports a wide range of both raster-centric and transparency-aware tasks within a single model. Notably, we observe that the model can perform transparent image editing in a zero-shot manner. To systematically evaluate this new task, we introduce TransEdit-Bench, the first benchmark specifically designed for transparent image editing. Extensive experiments across various tasks demonstrate strong performance and highlight the great potential of transparency-native image modeling as a unified paradigm for future image synthesis and editing.

In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets within the general domain before being transferred to the chest X-ray domain. Following a prominent VLM, we unify various domain-specific tasks into a simple sequence-to-sequence schema. It enables the model to effectively learn the required knowledge and skills from limited resources in the domain. Demonstrating superior performance on the benchmark datasets provided by the BioNLP shared task (Delbrouck et al., 2023), our model benefits from its training across multiple tasks and domains. With subtle techniques including ensemble and factual calibration, our system achieves first place on the RadSum23 leaderboard for the hidden test set.

This paper tackles an emerging and challenging problem of long video temporal grounding (VTG) that localizes video moments related to a natural language (NL) query. Compared with short videos, long videos are also highly demanded but less explored, which brings new challenges in higher inference computation cost and weaker multi-modal alignment. To address these challenges, we propose CONE, an efficient COarse-to-fiNE alignment framework. CONE is a plug-and-play framework on top of existing VTG models to handle long videos through a sliding window mechanism. Specifically, CONE (1) introduces a query-guided window selection strategy to speed up inference, and (2) proposes a coarse-to-fine mechanism via a novel incorporation of contrastive learning to enhance multi-modal alignment for long videos. Extensive experiments on two large-scale long VTG benchmarks consistently show both substantial performance gains (e.g., from 3.13 to 6.87% on MAD) and state-of-the-art results. Analyses also reveal higher efficiency as the query-guided window selection mechanism accelerates inference time by 2x on Ego4D-NLQ and 15x on MAD while keeping SOTA results. Codes have been released at https://github.com/houzhijian/CONE.

Generating image captions with user intention is an emerging need. The recently published Localized Narratives dataset takes mouse traces as another input to the image captioning task, which is an intuitive and efficient way for a user to control what to describe in the image. However, how to effectively employ traces to improve generation quality and controllability is still under exploration. This paper aims to solve this problem by proposing a novel model called LoopCAG, which connects Contrastive constraints and Attention Guidance in a Loop manner, engaged explicit spatial and temporal constraints to the generating process. Precisely, each generated sentence is temporally aligned to the corresponding trace sequence through a contrastive learning strategy. Besides, each generated text token is supervised to attend to the correct visual objects under heuristic spatial attention guidance. Comprehensive experimental results demonstrate that our LoopCAG model learns better correspondence among the three modalities (vision, language, and traces) and achieves SOTA performance on trace-controlled image captioning task. Moreover, the controllability and explainability of LoopCAG are validated by analyzing spatial and temporal sensitivity during the generation process.