AI Breakdown

• Arxiv paper - MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks

  In this episode, we discuss MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks by Jiacheng Chen, Tianhao Liang, Sherman Siu, Zhengqing Wang, Kai Wang, Yubo Wang, Yuansheng Ni, Wang Zhu, Ziyan Jiang, Bohan Lyu, Dongfu Jiang, Xuan He, Yuan Liu, Hexiang Hu, Xiang Yue, Wenhu Chen. The paper introduces MEGA-BENCH, a comprehensive evaluation suite featuring over 500 real-world multimodal tasks to address diverse daily user needs. It includes more than 8,000 samples curated by 16 expert annotators, utilizing a variety of output formats such as numbers, phrases, and code instead of standard multiple-choice questions. MEGA-BENCH aims to provide high-quality, diverse data for cost-effective and accurate model evaluation across a wide range of multimodal tasks.

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  4:24
• Arxiv paper - TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models

  In this episode, we discuss TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models by Mark YU, Wenbo Hu, Jinbo Xing, Ying Shan. TrajectoryCrafter is a new method that precisely redirects camera paths in monocular videos by separating view changes from content generation. It uses a dual-stream conditional video diffusion model that combines point cloud renders with source videos to ensure accurate views and coherent 4D content. By training on a hybrid dataset of monocular and multi-view videos with a double-reprojection strategy, TrajectoryCrafter achieves robust performance across diverse scenes.

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  4:22
• Arxiv paper - PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving

  In this episode, we discuss PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving by Mihir Parmar, Xin Liu, Palash Goyal, Yanfei Chen, Long Le, Swaroop Mishra, Hossein Mobahi, Jindong Gu, Zifeng Wang, Hootan Nakhost, Chitta Baral, Chen-Yu Lee, Tomas Pfister, Hamid Palangi. The paper introduces **PlanGEN**, a versatile agent framework designed to tackle complex planning problems by incorporating constraint, verification, and selection agents. PlanGEN enhances existing inference-time algorithms through constraint-guided iterative verification and dynamically selects the optimal algorithm based on the complexity of each instance. Experimental results show that PlanGEN significantly outperforms leading baselines across multiple benchmarks, achieving state-of-the-art performance by effectively improving verification processes and adaptive algorithm selection.

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  5:27
• Arxiv paper - VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing

  In this episode, we discuss VideoGrain: Modulating Space-Time Attention for Multi-grained Video Editing by Xiangpeng Yang, Linchao Zhu, Hehe Fan, Yi Yang. The paper introduces VideoGrain, a zero-shot method that enhances multi-grained video editing by modulating space-time attention mechanisms for class-, instance-, and part-level modifications. It addresses challenges like semantic misalignment and feature coupling by improving text-to-region control and optimizing feature separation within diffusion models. Extensive experiments demonstrate that VideoGrain achieves state-of-the-art performance in real-world video editing scenarios.

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  5:13
• Arxiv paper - ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models

  In this episode, we discuss ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models by Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma, Akash Gupta, Samuel Roberts, Ioana Croitoru, Simion-Vlad Bogolin, Jialu Tang, Florian Langer, Vyas Raina, Vatsal Raina, Hanyi Xiong, Vishaal Udandarao, Jingyi Lu, Shiyang Chen, Sam Purkis, Tianshuo Yan, Wenye Lin, Gyungin Shin, Qiaochu Yang, Anh Totti Nguyen, Kai Han, Samuel Albanie. The paper reveals that Large Multimodal Models (LMMs) have significant difficulties with image interpretation and spatial reasoning, often underperforming compared to young children or animals. To address this gap, the authors introduce ZeroBench, a challenging visual reasoning benchmark comprising 100 carefully designed questions and 334 subquestions that current LMMs cannot solve. Evaluation of 20 models resulted in a 0% score on ZeroBench, and the benchmark is publicly released to stimulate advancements in visual understanding.

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