Xiaofeng Wang

Bohan Li, Xin Jin, Jianan Wang et al.

Recent diffusion models have demonstrated remarkable performance in both 3D scene generation and perception tasks. Nevertheless, existing methods typically separate these two processes, acting as a data augmenter to generate synthetic data for downstream perception tasks. In this work, we propose OccScene, a novel mutual learning paradigm that integrates fine-grained 3D perception and high-quality generation in a unified framework, achieving a cross-task win-win effect. OccScene generates new and consistent 3D realistic scenes only depending on text prompts, guided with semantic occupancy in a joint-training diffusion framework. To align the occupancy with the diffusion latent, a Mamba-based Dual Alignment module is introduced to incorporate fine-grained semantics and geometry as perception priors. Within OccScene, the perception module can be effectively improved with customized and diverse generated scenes, while the perception priors in return enhance the generation performance for mutual benefits. Extensive experiments show that OccScene achieves realistic 3D scene generation in broad indoor and outdoor scenarios, while concurrently boosting the perception models to achieve substantial performance improvements in the 3D perception task of semantic occupancy prediction.

Qianhong Guo, Wei Xie, Xiaofang Cai et al.

Although large language models (LLMs) demonstrate remarkable capabilities across various tasks, evaluating their capabilities remains a challenging task. Existing evaluation methods suffer from issues such as data contamination, black-box operation, and subjective preference. These issues make it difficult to evaluate the LLMs' true capabilities comprehensively. To tackle these challenges, we propose a novel benchmark-free evaluation paradigm, LLM-Crowdsourced. It utilizes LLMs to generate questions, answer independently, and evaluate mutually. This method integrates four key evaluation criteria: dynamic, transparent, objective, and professional, which existing evaluation methods cannot satisfy simultaneously. Experiments on eight mainstream LLMs across mathematics and programming verify the advantages of our method in distinguishing LLM performance. Furthermore, our study reveals several novel findings that are difficult for traditional methods to detect, including but not limited to: (1) Gemini demonstrates the highest original and professional question-design capabilities among others; (2) Some LLMs exhibit ''memorization-based answering'' by misrecognizing questions as familiar ones with a similar structure; (3) LLM evaluation results demonstrate high consistency (robustness).

Xiaofeng Wang, Daniel Graziotin, Juha Rikkilä et al.

Group brainstorming is a well-known idea generation technique, which plays a key role in software development processes. Despite this, the relevant literature has had little to offer in advancing our understanding of the effectiveness of group brainstorming sessions. In this paper we present a research-in-progress on brainstorming while walking, which is a practice built upon the relationship between thinking and walking. The objective is to better understand how to conduct group brainstorming effectively. We compared two brainstorming sessions, one performed during a mountain walk, the other traditionally in a room. Three preliminary findings are obtained: walking can lead to an effective idea generation session; brainstorming while walking can encourage team members to participate in and contribute to the session in an equal manner; and it can help a team to maintain sustainable mental energy. Our study opens up an avenue for future exploration of effective group brainstorming practices.