Category: Uncategorized
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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…
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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,…
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Arxiv paper – Teaching Language Models to Critique via Reinforcement Learning
In this episode, we discuss Teaching Language Models to Critique via Reinforcement Learning by Zhihui Xie, Jie chen, Liyu Chen, Weichao Mao, Jingjing Xu, Lingpeng Kong. The paper presents CTRL, a framework that uses reinforcement learning to train critic models which provide feedback for improving code generated by large language models without needing human input.…
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Arxiv paper – PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling
In this episode, we discuss PANDAS: Improving Many-shot Jailbreaking via Positive Affirmation, Negative Demonstration, and Adaptive Sampling by Avery Ma, Yangchen Pan, Amir-massoud Farahmand. The paper introduces PANDAS, a hybrid technique that enhances many-shot jailbreaking by altering fabricated dialogues with positive affirmations, negative demonstrations, and optimized adaptive sampling tailored to specific prompts. Experimental results on…
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Arxiv paper – VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation
In this episode, we discuss VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation by Sixiao Zheng, Zimian Peng, Yanpeng Zhou, Yi Zhu, Hang Xu, Xiangru Huang, Yanwei Fu. The paper presents VidCRAFT3, a new framework for image-to-video generation that allows simultaneous control over camera motion, object movement, and lighting direction. It addresses previous limitations…
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Arxiv paper – Heuristically Adaptive Diffusion-Model Evolutionary Strategy
In this episode, we discuss Heuristically Adaptive Diffusion-Model Evolutionary Strategy by Benedikt Hartl, Yanbo Zhang, Hananel Hazan, Michael Levin. The paper explores the connection between diffusion models and evolutionary algorithms, highlighting that both generate high-quality samples through iterative refinement of random initial states. By integrating deep learning-based diffusion models into evolutionary processes, the authors enhance…
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Arxiv paper – Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
In this episode, we discuss Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach by Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein. The paper presents a new language model architecture that enhances test-time computation by iteratively reasoning in latent space using…
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Arxiv paper – EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents
In this episode, we discuss EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents by Rui Yang, Hanyang Chen, Junyu Zhang, Mark Zhao, Cheng Qian, Kangrui Wang, Qineng Wang, Teja Venkat Koripella, Marziyeh Movahedi, Manling Li, Heng Ji, Huan Zhang, Tong Zhang. The paper presents **EMBODIEDBENCH**, a comprehensive benchmark with 1,128 tasks across…
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Arxiv paper – VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection
In this episode, we discuss VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection by Songhao Han, Wei Huang, Hairong Shi, Le Zhuo, Xiu Su, Shifeng Zhang, Xu Zhou, Xiaojuan Qi, Yue Liao, Si Liu. The paper introduces VideoEspresso, a high-quality, large-scale VideoQA dataset that maintains essential spatial and temporal details…
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Arxiv paper – VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
In this episode, we discuss VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models by Hila Chefer, Uriel Singer, Amit Zohar, Yuval Kirstain, Adam Polyak, Yaniv Taigman, Lior Wolf, Shelly Sheynin. Generative video models typically prioritize appearance accuracy over motion coherence, limiting their ability to capture realistic dynamics. The paper presents VideoJAM, a…
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Arxiv paper – HunyuanVideo: A Systematic Framework For Large Video Generative Models
In this episode, we discuss HunyuanVideo: A Systematic Framework For Large Video Generative Models by Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jiangfeng Xiong, Xin Li, Bo Wu, Jianwei Zhang, Kathrina Wu, Qin Lin, Junkun Yuan, Yanxin Long, Aladdin Wang, Andong Wang, Changlin Li, Duojun Huang, Fang Yang, Hao Tan,…
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Arxiv paper – s1: Simple test-time scaling
In this episode, we discuss s1: Simple test-time scaling by Niklas Muennighoff, Zitong Yang, Weijia Shi, Xiang Lisa Li, Li Fei-Fei, Hannaneh Hajishirzi, Luke Zettlemoyer, Percy Liang, Emmanuel Candès, Tatsunori Hashimoto. The paper introduces a straightforward method for test-time scaling in language models to enhance reasoning performance by utilizing additional computational resources during inference. The…
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Arxiv paper – Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
In this episode, we discuss Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation by The authors of the paper are the **Hunyuan3D Team**. Specific contributor names are indicated to be listed at the end of the full report.. Hunyuan3D 2.0 is a large-scale 3D synthesis system featuring Hunyuan3D-DiT for generating detailed…
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Arxiv paper – MatAnyone: Stable Video Matting with Consistent Memory Propagation
In this episode, we discuss MatAnyone: Stable Video Matting with Consistent Memory Propagation by Peiqing Yang, Shangchen Zhou, Jixin Zhao, Qingyi Tao, Chen Change Loy. The paper introduces **MatAnyone**, a robust framework for target-assigned video matting that overcomes challenges posed by complex or ambiguous backgrounds without relying on auxiliary inputs. It employs a memory-based approach…
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Arxiv paper – Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate
In this episode, we discuss Critique Fine-Tuning: Learning to Critique is More Effective than Learning to Imitate by Yubo Wang, Xiang Yue, Wenhu Chen. The paper introduces Critique Fine-Tuning (CFT), a novel approach where language models are trained to critique noisy responses instead of simply imitating correct ones, inspired by human critical thinking. Using a…
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Arxiv paper – Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs
In this episode, we discuss Thoughts Are All Over the Place: On the Underthinking of o1-Like LLMs by Yue Wang, Qiuzhi Liu, Jiahao Xu, Tian Liang, Xingyu Chen, Zhiwei He, Linfeng Song, Dian Yu, Juntao Li, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu. The paper identifies “underthinking” in large language models like…
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Arxiv paper – MetaMorph: Multimodal Understanding and Generation via Instruction Tuning
In this episode, we discuss MetaMorph: Multimodal Understanding and Generation via Instruction Tuning by Shengbang Tong, David Fan, Jiachen Zhu, Yunyang Xiong, Xinlei Chen, Koustuv Sinha, Michael Rabbat, Yann LeCun, Saining Xie, Zhuang Liu. The paper introduces Visual-Predictive Instruction Tuning (VPiT), which enhances pretrained large language models to generate both text and visual tokens by…
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Arxiv paper – Improving Video Generation with Human Feedback
In this episode, we discuss Improving Video Generation with Human Feedback by Jie Liu, Gongye Liu, Jiajun Liang, Ziyang Yuan, Xiaokun Liu, Mingwu Zheng, Xiele Wu, Qiulin Wang, Wenyu Qin, Menghan Xia, Xintao Wang, Xiaohong Liu, Fei Yang, Pengfei Wan, Di Zhang, Kun Gai, Yujiu Yang, Wanli Ouyang. The paper introduces a pipeline that utilizes…
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Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
In this episode, we discuss Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling by The authors of the paper are: – Xiaokang Chen – Zhiyu Wu – Xingchao Liu – Zizheng Pan – Wen Liu – Zhenda Xie – Xingkai Yu – Chong Ruan. The paper introduces Janus-Pro, an enhanced version of…
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Arxiv paper – DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
In this episode, we discuss DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning by DeepSeek-AI. The paper introduces DeepSeek-R1-Zero, a reasoning model trained solely with large-scale reinforcement learning, which exhibits strong reasoning abilities but struggles with readability and language mixing. To overcome these limitations, the authors developed DeepSeek-R1 by adding multi-stage training and cold-start…
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Arxiv paper – Can We Generate Images with CoT? Let’s Verify and Reinforce Image Generation Step by Step
In this episode, we discuss Can We Generate Images with CoT? Let’s Verify and Reinforce Image Generation Step by Step by Ziyu Guo, Renrui Zhang, Chengzhuo Tong, Zhizheng Zhao, Peng Gao, Hongsheng Li, Pheng-Ann Heng. The paper investigates the use of Chain-of-Thought (CoT) reasoning to improve autoregressive image generation through techniques like test-time computation scaling,…
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Arxiv paper – Improving Factuality with Explicit Working Memory
In this episode, we discuss Improving Factuality with Explicit Working Memory by Mingda Chen, Yang Li, Karthik Padthe, Rulin Shao, Alicia Sun, Luke Zettlemoyer, Gargi Gosh, Wen-tau Yih. The paper presents Ewe, a novel method that incorporates explicit working memory into large language models to improve factuality in long-form text generation by updating memory in…
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Arxiv paper – Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control
In this episode, we discuss Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video Generation Control by Zekai Gu, Rui Yan, Jiahao Lu, Peng Li, Zhiyang Dou, Chenyang Si, Zhen Dong, Qifeng Liu, Cheng Lin, Ziwei Liu, Wenping Wang, Yuan Liu. The paper introduces “Diffusion as Shader” (DaS), a novel approach that supports various video…
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Arxiv paper – FaceLift: Single Image to 3D Head with View Generation and GS-LRM
In this episode, we discuss FaceLift: Single Image to 3D Head with View Generation and GS-LRM by Weijie Lyu, Yi Zhou, Ming-Hsuan Yang, Zhixin Shu. FaceLift is a feed-forward approach for rapid and high-quality 360-degree head reconstruction using a single image, utilizing a multi-view latent diffusion model followed by a GS-LRM reconstructor to create 3D…
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Arxiv paper – GenHMR: Generative Human Mesh Recovery
In this episode, we discuss GenHMR: Generative Human Mesh Recovery by Muhammad Usama Saleem, Ekkasit Pinyoanuntapong, Pu Wang, Hongfei Xue, Srijan Das, Chen Chen. The paper introduces GenHMR, a novel generative framework for human mesh recovery (HMR) that addresses uncertainties in converting 2D images to 3D mesh. It employs a pose tokenizer and an image-conditional…