arXiv AI
Jul 13
Video Generation Models are General-Purpose Vision Learners
arXiv:2607. 09024v1 Announce Type: cross Abstract: Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models.
Image Generators are Generalist Vision Learners
arXiv:2604. 20329v3 Announce Type: replace-cross Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining.
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Hugging Face Trending Papers
Jul 7
Vision as Unified Multimodal Generation
We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal model, without task-specific architectures. Under this formulation, SenseNova-Vision uses natural-language instructions and optional visual prompts to specify tasks, target regions or views, and decoding conventions, and generates responses as text for symbolic outputs, images for dense spatial predictions, or mixed text-and-image outputs for compositional tasks.
arXiv Computer Vision
4d ago
OmniTaskonomy: When Does Visual Generation Improve Visual Understanding?
arXiv:2609.38079v1 Announce Type: new Abstract: Training a model to generate visual content can encourage it to learn rich perceptual capabilities related to geometry, spatial relationships, and obje...
arXiv AI
Jun 26
Semantic Generative Tuning for Unified Multimodal Models
arXiv:2605. 18714v2 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture.
Hugging Face Trending Papers
4d ago
OmniTaskonomy: When Does Visual Generation Improve Visual Understanding?
Training a model to generate visual content can encourage it to learn rich perceptual capabilities related to geometry, spatial relationships, and objectness; yet, its benefits for visual understandin...
arXiv AI
Sep 7
Cross-Task Generalization Between Understanding and Generation in Unified Vision-Language Models: A Controlled Study
The study investigates how unified vision‑language models (VLMs) can simultaneously support visual understanding and generation. Using controlled benchmarks (SmartWatch and modified CelebA) that pair VQA, captioning, and text‑to‑image tasks, the authors evaluate several LLM‑based architectures built on SigLIP and VQ‑VAE visual spaces. Results show that mixed training can improve both understanding and generation, but the gains depend on how well the visual input and output spaces are aligned; misaligned or distorted visual spaces can weaken or reverse these benefits. The paper also demonstrates that balancing data across tasks and controlling attribute frequencies can help recover underrepresented visual concepts, and that the transfer is driven more by the base language model’s learned relationships than by visual adapters.
arXiv Computer Vision
Sep 24
UVU: Improving Multimodal Understanding via Vision-Language Unified Autoregressive Paradigm
UVU is a vision-language unified autoregressive framework that integrates visual supervision directly into the pre-training stage of multimodal large language models. By using continuous visual encoding and a large-scale iterative hierarchical clustering algorithm to build a pixel-level visual codebook, UVU enables lossless representation of visual inputs and autoregressive generation of pixel-level image tokens alongside textual tokens. This approach synergizes pixel-level visual perception with semantic-level visual understanding, allowing models to internalize visual reconstruction capabilities and improve multimodal understanding performance.
arXiv AI
Jul 29
Argus-Unified: Towards A Compact and Economical Unified Model for Image Understanding and Generation
arXiv:2607. 25527v1 Announce Type: cross Abstract: Unifying visual understanding and generation in one model holds immense promise, but remains challenging and expensive due to heavy compute and data demands and conflicts between the visual features needed for these two capabilities.
arXiv Computer Vision
Aug 25
Traj-VLN: Learning Pixel-Space Interaction via Autoregressive Trajectory Generation
arXiv:2607.10744v5 Announce Type: replace Abstract: Benefiting from the powerful priors embedded in large-scale pre-training data and the emerging commonsense reasoning ability, large language models...
arXiv AI
Aug 25
Think with Structured Grounding: Perceptual Reinforcement Learning for Chart and Visual-Tabular Understanding
arXiv:2608.22429v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this relian...
arXiv Computer Vision
Sep 11
SenseNova-U1.5: Towards Native Unified Visual Intelligence
SenseNova-U1.5 is an 8B‑MoT native unified multimodal model that can understand, reason about, and generate visual content without using an encoder or VAE. It improves visual fidelity and text rendering through spatially coherent patch reconstruction, large‑scale training on curated generation and editing data, and native resolutions up to 4K. Post‑training, specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing are optimized and distilled into a multi‑expert framework, yielding advances in image fidelity, complex composition, multi‑reference editing, and instruction following.
arXiv AI
Jun 3
Vision Inference Former: Sustaining Visual Consistency in Multimodal Large Language Models
arXiv:2605. 18160v2 Announce Type: replace-cross Abstract: In recent years, multimodal large language models (MLLMs) have achieved remarkable progress, primarily attributed to effective paradigms for integrating visual and textual information.