Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjects according to user instructions. However, evaluating these models remains a significant challenge. Existing benchmarks exhibit critical limitations: 1) insufficient diversity and comprehensiveness in subject images, 2) inadequate granularity in assessing model performance across different subject difficulty levels and prompt scenarios, and 3) a profound lack of actionable insights and diagnostic guidance for subsequent model refinement. To address these limitations, we propose DSH-Bench, a comprehensive benchmark that enables systematic multi-perspective analysis of subject-driven T2I models through four principal innovations: 1) a hierarchical taxonomy sampling mechanism ensuring comprehensive subject representation across 58 fine-grained categories, 2) an innovative classification scheme categorizing both subject difficulty level and prompt scenario for granular capability assessment, 3) a novel Subject Identity Consistency Score (SICS) metric demonstrating a 9.4\% higher correlation with human evaluation compared to existing measures in quantifying subject preservation, and 4) a comprehensive set of diagnostic insights derived from the benchmark, offering critical guidance for optimizing future model training paradigms and data construction strategies. Through an extensive empirical evaluation of 19 leading models, DSH-Bench uncovers previously obscured limitations in current approaches, establishing concrete directions for future research and development.
VisionQ: VLM-as-a-Judge Taxonomy, Dataset and Benchmark for Qualitative Analysis in Computer Vision
University of Information Technology, VNU-HCM（越南国立胡志明市大学信息技术大学）；University College Cork（科克大学）；Hanoi University of Science and Technology（河内科技大学）；Trinity College Dublin（都柏林圣三一大学）
OmniOpt: Taxonomy, Geometry, and Benchmarking of Modern Optimizers
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai University（上海大学）；Westlake University（西湖大学）；Shanghai Jiao Tong University（上海交通大学）；UCAS（中国科学技术大学）；Zhejiang University（浙江大学）；Southern University of Science and Technology（南方科技大学）
RobotEQ-Video: A Video-Centric Benchmark for Social Proactive Intelligence with World-State Taxonomy
State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（同济大学自主智能无人系统全国重点实验室）；Tsinghua University（清华大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；University of Science and Technology of China（中国科学技术大学）
Engineering Reasoning and Instruction (ERI) Benchmark: A Large Taxonomy-driven Dataset for Foundation Models and Agents
Clemson University（克莱姆森大学）；University of Manitoba（曼尼托巴大学）；Northwestern University（西北大学）；American University of Sharjah（沙迦美国大学）；Michigan State University（密歇根州立大学）；Medical University of Gdańsk（格但斯克医科大学）
Benchmarking Autonomy in Scientific Experiments: A Hierarchical Taxonomy for Autonomous Large-Scale Facilities
University of Greenwich, Old Royal Naval College, Park Row, London, SE10 9LS, United Kingdom（格林威治大学，皇家海军学院，伦敦公园行，SE10 9LS，英国）；ISIS Neutron & Muon Source, Rutherford Appleton Laboratory, Didcot, OX11 0QX, United Kingdom（ISIS中子与穆子源，拉瑟福德-苹果顿实验室，迪茨，OX11 0QX，英国）；The Faraday Institution, Harwell Science and Innovation Campus, Didcot, OX11 0RA, United Kingdom（法拉第机构，哈威尔科学与创新校园，迪茨，OX11 0RA，英国）
GPU-Accelerated Algorithms for Graph Vector Search: Taxonomy, Empirical Study, and Research Directions
Hong Kong Polytechnic University, Hong Kong SAR（香港理工大学）；The Hong Kong University of Science and Technology, Hong Kong SAR（香港理工大学）；Huazhong University of Science and Technology, China（华中科技大学）
Ericsson Research, Chennai, India（印度钦奈爱立信研究院）；University of Colorado, Boulder, Colorado, USA（美国科罗拉多州博尔德市科罗拉多大学）
In E-commerce, it is a common practice to organize the product catalog using product taxonomy. This enables the buyer to easily locate the item they are looking for and also to explore various items available under a category. Product taxonomy is a tree structure with 3 or more levels of depth and several leaf nodes. Product categorization is a large scale classification task that assigns a category path to a particular product. Research in this area is restricted by the unavailability of good real-world datasets and the variations in taxonomy due to the absence of a standard across the differ…
TMT-VIS: Taxonomy-aware Multi-dataset Joint Training for Video Instance Segmentation
The University of Hong Kong（香港大学）；University of California, Merced（加州大学默塞德分校）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；SenseTime Research（商汤科技研究院）
Facebook AI（脸书人工智能研究院）；National University of Singapore（新加坡国立大学）
This paper presents a novel task together with a new benchmark for detecting generic, taxonomy-free event boundaries that segment a whole video into chunks. Conventional work in temporal video segmentation and action detection focuses on localizing pre-defined action categories and thus does not scale to generic videos. Cognitive Science has known since last century that humans consistently segment videos into meaningful temporal chunks. This segmentation happens naturally, without pre-defined event categories and without being explicitly asked to do so. Here, we repeat these cognitive experim…
Fudan University（复旦大学）；Tongji University（同济大学）；Huaiyin Normal University（淮阴师范学院）；Tencent AI Lab（腾讯人工智能实验室）
Few-shot object detection (FSOD) is to detect objects with a few examples. However, existing FSOD methods do not consider hierarchical fine-grained category structures of objects that exist widely in real life. For example, animals are taxonomically classified into orders, families, genera and species etc. In this paper, we propose and solve a new problem called hierarchical few-shot object detection (Hi-FSOD), which aims to detect objects with hierarchical categories in the FSOD paradigm. To this end, on the one hand, we build the first large-scale and high-quality Hi-FSOD benchmark dataset H…
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Hospital Essen (AöR)（埃森大学医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Imaging（亥姆霍兹成像机构）
