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Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing
Hao Fei, Shengqiong Wu, Hanwang Zhang, Tat-Seng Chua, Shuicheng Yan
TL;DR
Existing vision LLMs often separate image and video support and cover only a limited range of vision tasks. VITRON combines an LLM, visual encoders and specialists, hybrid message passing, grounding, and cross-task feature sharing; across 12 tasks and 22 datasets, it performs on par with or surpasses singleton specialists on specific tasks.
Problem
Method
VITRON combines image, video, and regional encoders, an LLM, visual specialists, hybrid instruction passing, pixel-level grounding, and cross-task feature sharing.
Source: §1 · p. 3Results
Across 12 tasks and 22 datasets, VITRON performs on par with or surpasses singleton state-of-the-art specialists on specific tasks.
Source: §1 · p. 3, §7 · p. 10Takeaways & Limitations
VITRON demonstrates a unified pixel-level vision LLM spanning understanding, generation, segmentation, and editing of images and videos.
Source: §1 · p. 3, §7 · p. 10
Abstract
from arXiv · showhide
Recent developments of vision large language models (LLMs) have seen remarkable progress, yet still encounter challenges towards multimodal generalists, such as coarse-grained instance-level understanding, lack of unified support for both images and videos, and insufficient coverage across various vision tasks. In this paper, we present VITRON, a universal pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing of both static images and dynamic videos. Building on top of an LLM backbone, VITRON incorporates encoders for images, videos, and pixel-level regional visuals within its frontend modules, while employing state-of-the-art visual specialists as its backend, via which VITRON supports a spectrum of vision end tasks, spanning visual comprehension to visual generation, from low level to high level. To ensure an effective and precise message passing from LLM to backend modules for function invocation, we propose a novel hybrid method by simultaneously integrating discrete textual instructions and continuous signal embeddings. Further, we design various pixel-level spatiotemporal vision-language alignment learning for VITRON to reach the best fine-grained visual capability. Finally, a cross-task synergy module is advised to learn to maximize the task-invariant fine-grained visual features, enhancing the synergy between different visual tasks. Demonstrated over 12 visual tasks and evaluated across 22 datasets, VITRON showcases its extensive capabilities in the four main vision task clusters. Overall, this work illuminates the great potential of developing a more unified multimodal generalist. Project homepage: https://vitron-llm.github.io/
1 Introduction
VITRON targets fragmented vision LLM capabilities by unifying images and videos, fine-grained visual tasks, and downstream specialist modules. It combines hybrid instruction passing, pixel-level training, and cross-task feature sharing; reported evaluations span 12 tasks and 22 datasets.
1 Introduction
- Existing vision LLMs often separate images from videos and support limited vision functions, while interactive creation can require generation, editing, and video operations.The introduction also identifies precise instruction transfer and cooperation among tasks as requirements for a generalist.Source: §1 · pp. 2–3
- VITRON combines an LLM with image, video, and regional-input encoders and specialist decoders for understanding, generation, segmentation, and editing.Its stated scope covers pixel-level capabilities for both images and videos.Source: §1 · p. 3
- Hybrid message passing sends discrete instructions and continuous signal embeddings, while cross-task synergy shares task-invariant fine-grained visual features.The paper also proposes pixel-level spatiotemporal vision-language alignment to develop fine-grained visual capability.Source: §1 · pp. 3–4
- Training combines frontend and backend alignment, invocation and embedding tuning, fine-grained grounding instruction tuning, and adversarial feature decoupling.The stated aim is to improve pixel-level perception and synergy between tasks.Source: §1 · p. 3
- Across 12 tasks and 22 datasets, VITRON performs on par with or surpasses singleton state-of-the-art specialists on specific tasks.The paper reports extensive capabilities across vision tasks and says further analyses examine the system’s design choices.Source: §1 · p. 3
2 Related Work
Prior vision models and MLLMs cover different levels of visual understanding and generation, but the paper identifies gaps in unified image-video support and vision-task breadth. It also describes complementary strengths and weaknesses in text-based and embedding-based message passing.
2 Related Work
- Vision models address understanding, segmentation, and generation, while MLLMs add language-based semantic processing and some generation or pixel-level grounding capabilities.The related-work discussion cites ViT and CLIP for vision understanding, SAM and SEEM for segmentation, and diffusion-based models for generation.Source: §2 · p. 4
- Existing vision LLMs often support either images or videos, and many focus on understanding rather than comprehensive pixel-level operations.The paper notes that NExT-GPT supports modality unification but falls short in pixel-level understanding and broad vision operations.Source: §2 · p. 4
- Text instructions convey task commands but struggle with modality-specific signals, while continuous embeddings carry task features but may not convey execution intent accurately.The paper proposes combining the two message-passing approaches.Source: §2 · p. 5
3 Architecture of VITRON
VITRON uses an encoder–LLM–decoder architecture: visual and language inputs reach a central LLM, which directs specialist modules for vision tasks. The frontend represents image, video, and user-specified regional inputs, while the LLM passes both instructions and feature embeddings downstream.
3 Architecture of VITRON
- VITRON follows an encoder–LLM–decoder architecture with frontend encoders, a central LLM, and backend modules for vision manipulation.The LLM handles semantic understanding and text generation.Source: §3 · p. 5
3.1 Frontend Vision-Language Encoding
- Image and video encoders use CLIP ViT-L/14@336px, while a regional pixel-aware extractor encodes user sketches, clicks, boxes, polygons, or scribbles.The video encoder processes frames independently and averages representations over time; region embeddings also encode spatial geometry.Source: §3.1 · p. 5
3.2 Core LLM
- The Vicuna 7B LLM reasons over language and visual inputs, then produces user responses, module-invocation text, and feature embeddings.The embeddings include task-specific features and task-invariant fine-grained visual-language features passed to backend modules.Source: §3.2 · p. 5
3.3 Backend Visual Specialists
- Backend specialists include GLIGEN for image generation and editing, SEEM for image and video segmentation, ZeroScope and I2VGen-XL for video generation, and StableVideo for video editing.LLM instructions select the module, while embeddings assist its execution.Source: §3.3 · pp. 5–6
4 Pixel-aware Synergistic Vision-Language Understanding Tuning
VITRON training proceeds from basic multimodal comprehension and generation to fine-grained spatial-temporal grounding and cross-task synergy learning. The final stage separates task-specific features from shared fine-grained features to encourage cooperation among visual tasks.
4 Pixel-aware Synergistic Vision-Language Understanding Tuning
- Training has three stages: basic multimodal capability learning, fine-grained grounding instruction tuning, and cross-task synergy learning.The stages aim to maximize shared fine-grained features across tasks.Source: §4 · p. 6
4.1 Basic Multimodal Comprehension and Generation Skill Training
- Basic training aligns vision and language, tunes text instructions for module invocation, and aligns LLM embeddings with visual-module input encoders.The instruction-tuning set contains 55,000+ samples.Source: §4.1 · p. 6
4.2 Fine-grained Spatiotemporal Vision Grounding Instruction Tuning
- Fine-grained grounding trains image spatial grounding, video spatial-temporal grounding, and grounding-aware vision QA based on grounded results.The tasks include grounded captioning and referring segmentation for images, and grounded captioning and referring tracking for videos.Source: §4.2 · p. 6
4.3 Cross-task Synergy Learning
- Cross-task synergy learning adversarially separates task-specific from task-invariant fine-grained features and encourages the shared representation to obscure task identity.Specialists predict tasks using concatenated features, while a discriminator attempts to identify the task from the shared features alone.Source: §4.3 · pp. 6–7
5 Experiments
Experiments evaluate VITRON across four vision-task groups, spanning 12 tasks and 22 datasets. Results generally favor VITRON across segmentation, fine-grained understanding, generation, and editing, with a stated exception on some image-segmentation sets.
5 Experiments
- Experiments cover 12 tasks across 22 datasets, with training on 10× A100 (80G) GPUs and evaluations following baseline settings or similar ones.Additional experimental results are reported in Appendix §D.Source: §5 · p. 7
5.1 Results on Vision Segmentation
- VITRON exceeds NExT-Chat on most referring-image-segmentation sets, though it slightly trails on RefCOCO Val&TestA; it also surpasses video-grounding and tracking systems.The comparisons cover cIoU for image segmentation, mIoU for video spatial grounding, and video object segmentation on DAVIS 17 Test-Dev.Source: §5.1 · p. 7, Table 2 · p. 7, Table 3 · p. 7
5.2 Results on Fine-grained Vision Understanding
- VITRON surpasses the best baseline across datasets and metrics for image regional captioning, and achieves the highest performance among evaluated models on image-based VQA.The VQA comparison spans six datasets and contrasts models with and without pixel-wise grounding.Source: Table 5 · p. 7, §5.2 · p. 8, Table 6 · p. 8
- On video QA, VITRON outperforms the evaluated systems, including a video-grounding model that does not outperform a model without grounding.The authors interpret this comparison as indirect evidence of more accurate video grounding aiding video-semantic understanding.Source: §5.2 · p. 8, Table 7 · p. 8
5.3 Results on Vision Generation
- VITRON outperforms state-of-the-art systems on text-to-image, text-to-video, and image-to-video generation.The comparisons include MLLM and non-MLLM synthesizers; the cited tables cover COCO-caption, MSR-VTT, and UCF101.Source: §5.3 · p. 8, Table 8 · p. 8, Table 9 · p. 8
5.4 Results on Vision Editing
- VITRON performs more strongly than non-LLM expert systems across the reported MagicBrush image-editing metrics.The dataset includes complex removal, alteration, inpainting, and addition edits.Source: §5.4 · p. 8, Table 11 · p. 9
- In a manual video-editing evaluation scored by five people, VITRON outperforms two baselines on target-modification success and non-target-content fidelity.The paper notes that video editing lacks a standardized benchmark and evaluation method.Source: §5.4 · p. 9, Table 12 · p. 9
6 Discussions
The analyses test VITRON’s message-passing choices, fine-grained grounding-training strategies, and cross-task synergy. They find benefits from combining text and signal embeddings, from the grounding strategies, and from synergy learning, with gains varying by task.
Discrete Textual Instruction or Continuous Signal Embedding, Which Better?
- Across six tasks, using both text instructions and continuous signal embeddings performs consistently better overall than omitting either; text favors backend execution, while embeddings favor task performance.The hybrid is the default setting in the comparisons.Source: §6 · p. 9, Figure 4 · p. 9
How Much Does Each Fine-grained Visual Grounding Learning Contribute?
- Removing any of the three fine-grained visual-grounding learning strategies affects downstream tasks; grounding and referring-segmentation tuning relate to recognition, while grounding-aware QA tuning boosts cognition-level QA.Figure 5 separates image-task results in its top four panels from video-task results in its bottom four.Source: §6 · p. 9, Figure 5 · p. 10
Does VITRON Really Achieve Cross-task Synergy?
- Synergy learning positively influences overall performance, but pairwise task cooperation varies, with larger gains for tasks or backbones relying more on fine-grained visual features.Figure 6 encodes stronger synergy with deeper-colored cells.Source: §6 · p. 10, Figure 6 · p. 10
7 Conclusion
VITRON unifies image and video understanding, generation, segmentation, and editing, combining an LLM with visual encoders and specialist backend modules. Its conclusion reports broad capabilities across 12 tasks and 22 datasets.
7 Conclusion
- Across 12 visual tasks and 22 datasets, VITRON demonstrates capabilities in segmentation, fine-grained understanding, generation, and editing of images and videos.The authors conclude that the work illustrates the potential for a more unified vision-language generalist.Source: §7 · p. 10
A Details of Backbone Visual Modules/Specialists
- The backend-module appendix summarizes the specialists integrated for VITRON, including GLIGEN for image generation and editing, SEEM for segmentation, and video generation and editing modules.The cited passage names ZeroScope, I2VGen-XL, and StableVideo for video tasks.Source: Table 13 · p. 19, A Details of Backbone Visual… · p. 19
B Extensions of Pixel-aware Synergistic Vision-Language Understanding Learning
- The appendix section on pixel-aware synergistic vision-language understanding extends the details of Section 4 in the main article.The supplied passage does not specify the extension's contents.Source: B Extensions of Pixel-aware… · p. 19
B.1 Baisc MLLM Skill Training
VITRON's basic skill training aligns visual inputs with the LLM, teaches structured task invocation, and aligns embeddings with backend modules. Further tuning trains fine-grained image and video grounding and grounding-aware question answering.
B.1 Baisc MLLM Skill Training
- Image-caption, video-caption, and region-caption pairs train a frozen LLM to generate descriptions aligned with reference captions, mapping visual-language features into a shared space.The datasets include CC3M, WebVid, and RefCOCO.Source: §B.1 · p. 19
- Invocation tuning teaches the LLM to produce structured responses containing a user reply, module name, invocation command, and optional region coordinates.Training data covers varied user-input scenarios and totals more than 55,000 samples, including examples generated with GPT-4.Source: §B.1 · pp. 19–20
- Embedding-oriented alignment concatenates task-specific and task-invariant features for backend modules, and trains feature alignment with signal-token and caption losses.The described loss also includes conditional latent denoising.Source: §B.1 · pp. 20–21
B.2 Fine-grained Spatiotemporal Vision Grounding Instruction Tuning
- Image grounding tuning teaches the LLM to associate captions with image regions and output bounding boxes for referred objects.The box coordinates are normalized to [0, 1]; training uses Flickr30K Entities, RefCOCO, RefCOCO+, and RefCOCOg.Source: §B.2 · p. 21
- Video grounding extends spatial boxes with start and end frame numbers, supporting grounded video captioning and referring-object tracking.The described training data draws on LaSOT, GOT10K, DanceTrack, and MOT16.Source: §B.2 · p. 21
- Grounding-aware vision QA trains the LLM to answer semantic questions using grounded image or video content.The examples first identify and ground the question's target object before giving an answer.Source: §B.2 · p. 22
B.3 Cross-task Synergy Learning
Cross-task synergy learning combines task-specific and task-invariant features for task predictions, while adversarially refining the shared features to reduce recoverable task identity.
B.3 Cross-task Synergy Learning
- For each task prediction, VITRON concatenates task-specific features vp with task-invariant fine-grained features vs and sends the unified embedding to the downstream module.Source: §B.3 · p. 22
- A task discriminator predicts task identity from vs; it is a four-layer, 768-dimensional Transformer with a feedforward layer and Softmax.The task ID is the discriminator's prediction target.Source: §B.3 · p. 22
- Adversarial training aims to make the shared features difficult for the discriminator to use in predicting task identity.Source: §B.3 · p. 22
B.4 Overall Training Remarks
Overall training proceeds from basic multimodal comprehension and generation, through fine-grained image and video grounding, to cross-task synergy learning.
B.4 Overall Training Remarks
- Step 1 aligns the encoder and LLM, tunes text invocation, and then trains continuous embedding-oriented alignment between the LLM and decoder.Source: §B.4 · p. 23
- Step 2 trains image spatial grounding, then video spatiotemporal grounding, followed by grounding-aware vision QA.The sequence moves from image and video perception to cognition-level question answering.Source: §B.4 · p. 23
- Step 3 combines adversarial synergy training with end-task prediction loss after the system has developed competitive abilities across visual tasks.Source: §B.4 · p. 23
C Extended Experimental Settings
The experiments evaluate VITRON on representative downstream vision tasks under baseline-like settings, with targeted backend pre-training. Training uses encoder projection layers and LoRA fine-tuning, across three pre-training phases.
C Extended Experimental Settings
- Experiments cover only one or two representative tasks per category, using settings similar to baselines and established evaluation practices.Backend modules, including GLIGEN and SEEM, are pre-trained on their respective datasets before testing.Source: C Extended Experimental Settings · p. 23
- Training focuses on encoder linear projections and efficient language-model fine-tuning with LoRA.The backends are first targeted-pre-trained for evaluation.Source: C Extended Experimental Settings · p. 23
- The three pre-training phases take approximately 40, 35, and 10 hours, respectively, on 10–16 A100 80G GPUs.The first phase uses batch size 128 and a maximum learning rate of 3e-4; the next two use maximum learning rates of 1e-5.Source: C Extended Experimental Settings · p. 23
- The implementation uses Vicuna 7B v1.5, CLIP-ViT image and video encoders with patch size 14, and 336px inputs.The synergy-module task discriminator is a four-layer Transformer with 768-dimensional representations.Source: C Extended Experimental Settings · p. 23
D More Experiment Results
The appendix extends the experiments with video segmentation and fine-grained vision-understanding comparisons. It reports evaluation scopes and identifies the corresponding result tables, but the supplied passages do not state table outcomes.
D.1 Vision Segmentation
- Video object segmentation results are tabulated in Table 15, with comparisons on DAVIS 17 Test-Dev and Youtube-VOS 2019 Val.The text describes the comparison as involving VITRON and state-of-the-art systems.Source: §D.1 · p. 23, Table 15 · p. 24
D.2 Fine-grained Vision Understanding
- Image referring-expression comprehension accuracy is reported in Table 16.The supplied passage identifies three datasets but does not name them or report outcomes.Source: §D.2 · p. 24, Table 16 · p. 24
- Table 17 reports accuracy results for image-based visual question answering.The supplied passages do not state the table's outcomes.Source: Table 17 · p. 24
- Table 18 reports accuracy and confidence scores for video question answering.The supplied passages do not state the table's outcomes.Source: Table 18 · p. 24
E.1 Vision Segmentation
The section presents VITRON’s interactive vision capabilities through examples spanning segmentation, fine-grained understanding, generation, and editing of images and videos, and states that IRB approval was not applicable because the work involved neither crowdsourcing nor human subjects research.
E.1 Vision Segmentation
- VITRON segments sketched image regions into corresponding objects with bounding boxes and masks, and identifies, localizes, and tracks video targets from queries, including implicit descriptions.The examples illustrate interactive image segmentation and flexible video segmentation that captures user intent.Source: §E.1 · pp. 24–25, Figure 7 · p. 25, Figure 8 · p. 25
E.2 Fine-grained Vision Understanding
- VITRON demonstrates fine-grained understanding through regional image captioning and video grounding, using precise perception of video details to understand content semantics.The paper presents examples of describing image regions and grounding video details.Source: §E.2 · p. 25, Figure 9 · p. 26, Figure 10 · p. 26
E.3 Vision Generation
- VITRON supports multi-turn content creation, generating videos from text directly or from text-generated images that users can edit before video generation.The example illustrates consecutive operations across text, image, and video modalities.Source: Figure 11 · p. 26, §E.3 · p. 27
E.4 Vision Editing
- VITRON accepts textual instructions or sketches for precise image edits, maintains context across sequential edits, and modifies video subjects or styles.The examples cover both image and video editing.Source: Figure 12 · p. 27, §E.4 · p. 27, Figure 13 · p. 27
NeurIPS Paper Checklist
- The IRB checklist answer is NA because the paper does not involve crowdsourcing or research with human subjects.Source: NeurIPS Paper Checklist · p. 33