1 citations · 4 across the 16 of their papers we have counts for
18 papers
Agentic Context Learning with Self-Discovered Specification
Jike Zhong, Ming Li, Yuxiang Lai +8
Context learning is an emerging inference-time task where LLMs must learn and apply novel, task-specific knowledge from intricate contexts absent from pre-training; even frontier m…
EntityBench: Towards Entity-Consistent Long-Range Multi-Shot Video Generation
Ruozhen He, Meng Wei, Ziyan Yang +1
Multi-shot video generation extends single-shot generation to coherent visual narratives, yet maintaining consistent characters, objects, and locations across shots remains a chall…
Seedance 2.0: Advancing Video Generation for World Complexity
Team Seedance, De Chen, Liyang Chen +168
Seedance 2.0 is a new native multi-modal audio-video generation model, officially released in China in early February 2026. Compared with its predecessors, Seedance 1.0 and 1.5 Pro…
Growing Visual Generative Capacity for Pre-Trained MLLMs
Hanyu Wang, Jiaming Han, Ziyan Yang +6
Multimodal large language models (MLLMs) extend the success of language models to visual understanding, and recent efforts have sought to build unified MLLMs that support both unde…
NoiseShift: Resolution-Aware Noise Recalibration for Better Low-Resolution Image Generation
Ruozhen He, Moayed Haji-Ali, Ziyan Yang +1
Text-to-image diffusion models often degrade when sampled at resolutions outside the final training resolution set. Prior work has largely emphasized higher resolution generation,…
Mixture of Contexts for Long Video Generation
Shengqu Cai, Ceyuan Yang, Lvmin Zhang +10
Long video generation is fundamentally a long context memory problem: models must retain and retrieve salient events across a long range without collapsing or drifting. However, sc…
Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations
Jiaming Han, Hao Chen, Yang Zhao +6
This paper presents a multimodal framework that attempts to unify visual understanding and generation within a shared discrete semantic representation. At its core is the Text-Alig…
VINCIE: Unlocking In-context Image Editing from Video
Leigang Qu, Feng Cheng, Ziyan Yang +7
In-context image editing aims to modify images based on a contextual sequence comprising text and previously generated images. Existing methods typically depend on task-specific pi…
Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model
Team Seawead, Ceyuan Yang, Zhijie Lin +52
This technical report presents a cost-efficient strategy for training a video generation foundation model. We present a mid-sized research model with approximately 7 billion parame…
Synthetic Video Enhances Physical Fidelity in Video Synthesis
Qi Zhao, Xingyu Ni, Ziyu Wang +4
We investigate how to enhance the physical fidelity of video generation models by leveraging synthetic videos derived from computer graphics pipelines. These rendered videos respec…
Long Context Tuning for Video Generation
Yuwei Guo, Ceyuan Yang, Ziyan Yang +5
Recent advances in video generation can produce realistic, minute-long single-shot videos with scalable diffusion transformers. However, real-world narrative videos require multi-s…
Evaluating Text-to-Image and Text-to-Video Synthesis with a Conditional Fréchet Distance
Jaywon Koo, Jefferson Hernandez, Moayed Haji-Ali +2
Evaluating text-to-image and text-to-video models is challenging due to a fundamental disconnect: established metrics fail to jointly measure visual quality and semantic alignment…
PropTest: Automatic Property Testing for Improved Visual Programming
Jaywon Koo, Ziyan Yang, Paola Cascante-Bonilla +2
Visual Programming has recently emerged as an alternative to end-to-end black-box visual reasoning models. This type of method leverages Large Language Models (LLMs) to generate th…
Learning from Synthetic Data for Visual Grounding
Ruozhen He, Ziyan Yang, Paola Cascante-Bonilla +2
This paper extensively investigates the effectiveness of synthetic training data to improve the capabilities of vision-and-language models for grounding textual descriptions to ima…
Improved Visual Grounding through Self-Consistent Explanations
Ruozhen He, Paola Cascante-Bonilla, Ziyan Yang +2
Vision-and-language models trained to match images with text can be combined with visual explanation methods to point to the locations of specific objects in an image. Our work sho…
SCoRD: Subject-Conditional Relation Detection with Text-Augmented Data
Ziyan Yang, Kushal Kafle, Zhe Lin +3
We propose Subject-Conditional Relation Detection SCoRD, where conditioned on an input subject, the goal is to predict all its relations to other objects in a scene along with thei…
Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations
Ziyan Yang, Kushal Kafle, Franck Dernoncourt +1
We propose a margin-based loss for tuning joint vision-language models so that their gradient-based explanations are consistent with region-level annotations provided by humans for…
Chair Segments: A Compact Benchmark for the Study of Object Segmentation
Leticia Pinto-Alva, Ian K. Torres, Rosangel Garcia +2
Over the years, datasets and benchmarks have had an outsized influence on the design of novel algorithms. In this paper, we introduce ChairSegments, a novel and compact semi-synthe…