87 citations · 353 across the 31 of their papers we have counts for
32 papers · 1 filter
From Pixels to Words -- Towards Native One-Vision Models at Scale
Haiwen Diao, Jiahao Wang, Penghao Wu +18
Current vision-language models (VLMs) typically stitch together separate image encoders and language decoders via multi-stage alignment, a modular framework that inevitably fragmen…
LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence
Xiang An, Yin Xie, Feilong Tang +27
We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of mu…
VideoOdyssey: A Benchmark for Ultra-Long-Context and Omni-Modal Video Understanding
Haichen He, Jiayi Zhou, Sifeng Shang +3
Real-world long video understanding requires models to perform continuous tracking, information integration and memory retention over massive temporal spans within extreme video du…
OneVision-Encoder: Codec-Aligned Sparsity as a Foundational Principle for Multimodal Intelligence
Feilong Tang, Xiang An, Yunyao Yan +16
Hypothesis. Artificial general intelligence is, at its core, a compression problem. Effective compression demands resonance: deep learning scales best when its architecture aligns…
Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding
Yuanhan Zhang, Yunice Chew, Yuhao Dong +3
Human intelligence requires correctness and robustness, with the former being foundational for the latter. In video understanding, correctness ensures the accurate interpretation o…
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
Dian Zheng, Ziqi Huang, Hongbo Liu +9
Video generation has advanced significantly, evolving from producing unrealistic outputs to generating videos that appear visually convincing and temporally coherent. To evaluate t…
Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos
Kairui Hu, Penghao Wu, Fanyi Pu +5
Humans acquire knowledge through three cognitive stages: perceiving information, comprehending knowledge, and adapting knowledge to solve novel problems. Videos serve as an effecti…
LLaVA-Video: Video Instruction Tuning With Synthetic Data
Yuanhan Zhang, Jinming Wu, Wei Li +4
The development of video large multimodal models (LMMs) has been hindered by the difficulty of curating large amounts of high-quality raw data from the web. To address this, we pro…
LLaVA-OneVision: Easy Visual Task Transfer
Bo Li, Yuanhan Zhang, Dong Guo +8
We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT…
LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models
Feng Li, Renrui Zhang, Hao Zhang +5
Visual instruction tuning has made considerable strides in enhancing the capabilities of Large Multimodal Models (LMMs). However, existing open LMMs largely focus on single-image t…
Long Context Transfer from Language to Vision
Peiyuan Zhang, Kaichen Zhang, Bo Li +7
Video sequences offer valuable temporal information, but existing large multimodal models (LMMs) fall short in understanding extremely long videos. Many works address this by reduc…
WorldQA: Multimodal World Knowledge in Videos through Long-Chain Reasoning
Yuanhan Zhang, Kaichen Zhang, Bo Li +4
Multimodal information, together with our knowledge, help us to understand the complex and dynamic world. Large language models (LLM) and large multimodal models (LMM), however, st…
Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward
Ruohong Zhang, Liangke Gui, Zhiqing Sun +8
Preference modeling techniques, such as direct preference optimization (DPO), has shown effective in enhancing the generalization abilities of large language model (LLM). However,…
VBench: Comprehensive Benchmark Suite for Video Generative Models
Ziqi Huang, Yinan He, Jiashuo Yu +13
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable…
OtterHD: A High-Resolution Multi-modality Model
Bo Li, Peiyuan Zhang, Jingkang Yang +3
In this paper, we present OtterHD-8B, an innovative multimodal model evolved from Fuyu-8B, specifically engineered to interpret high-resolution visual inputs with granular precisio…
Multimodal Foundation Models for Zero-shot Animal Species Recognition in Camera Trap Images
Zalan Fabian, Zhongqi Miao, Chunyuan Li +10
Due to deteriorating environmental conditions and increasing human activity, conservation efforts directed towards wildlife is crucial. Motion-activated camera traps constitute an…
Octopus: Embodied Vision-Language Programmer from Environmental Feedback
Jingkang Yang, Yuhao Dong, Shuai Liu +8
Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. When integrated into an embodied agent, existing embodied VLM works e…
MMBench: Is Your Multi-modal Model an All-around Player?
Yuan Liu, Haodong Duan, Yuanhan Zhang +9
Large vision-language models (VLMs) have recently achieved remarkable progress, exhibiting impressive multimodal perception and reasoning abilities. However, effectively evaluating…
MIMIC-IT: Multi-Modal In-Context Instruction Tuning
Bo Li, Yuanhan Zhang, Liangyu Chen +5
High-quality instructions and responses are essential for the zero-shot performance of large language models on interactive natural language tasks. For interactive vision-language…
FunQA: Towards Surprising Video Comprehension
Binzhu Xie, Sicheng Zhang, Zitang Zhou +5
Surprising videos, such as funny clips, creative performances, or visual illusions, attract significant attention. Enjoyment of these videos is not simply a response to visual stim…
Latent Distribution Adjusting for Face Anti-Spoofing
Qinghong Sun, Zhenfei Yin, Yichao Wu +2
With the development of deep learning, the field of face anti-spoofing (FAS) has witnessed great progress. FAS is usually considered a classification problem, where each class is a…
Learning without Forgetting for Vision-Language Models
Da-Wei Zhou, Yuanhan Zhang, Yan Wang +4
Class-Incremental Learning (CIL) or continual learning is a desired capability in the real world, which requires a learning system to adapt to new tasks without forgetting former o…
Otter: A Multi-Modal Model with In-Context Instruction Tuning
Bo Li, Yuanhan Zhang, Liangyu Chen +5
Recent advances in Large Multimodal Models (LMMs) have unveiled great potential as visual assistants. However, most existing works focus on responding to individual instructions or…
What Makes Good Examples for Visual In-Context Learning?
Yuanhan Zhang, Kaiyang Zhou, Ziwei Liu
Large-scale models trained on broad data have recently become the mainstream architecture in computer vision due to their strong generalization performance. In this paper, the main…