An official website of the United States government
This work deals with the challenge of learning and reasoning over language and vision data for the related downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR). We design a novel cross-modality relevance module that is used in an end-to-end framework to learn the relevance representation between components of various input modalities under the supervision of a target task, which is more generalizable to unobserved data compared to merely reshaping the original representation space. In addition to modeling the relevance between the textual entities and visual entities, we model the higher-order relevance between entity relations in the text and object relations in the image. Our proposed approach shows competitive performance on two different language and vision tasks using public benchmarks and improves the state-of-the-art published results. The learned alignments of input spaces and their relevance representations by NLVR task boost the training efficiency of VQA task.
more »
« less
This content will become publicly available on February 7, 2026
QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation
We introduce Quantized Language-Image Pretraining (QLIP), a visual tokenization method that combines state-of-the-art reconstruction quality with state-of-the-art zero-shot image understanding. QLIP trains a binary-spherical-quantization-based autoencoder with reconstruction and language-image alignment objectives. We are the first to show that the two objectives do not need to be at odds. We balance the two loss terms dynamically during training and show that a two-stage training pipeline effectively mixes the large-batch requirements of image-language pre-training with the memory bottleneck imposed by the reconstruction objective. We validate the effectiveness of QLIP for multimodal understanding and text-conditioned image generation with a single model. Specifically, QLIP serves as a drop-in replacement for the visual encoder for LLaVA and the image tokenizer for LlamaGen with comparable or even better performance. Finally, we demonstrate that QLIP enables a unified mixed-modality auto-regressive model for understanding and generation.
more »
« less
- Award ID(s):
- 2505865
- PAR ID:
- 10631884
- Publisher / Repository:
- cs.CV
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Photographer, curator, and former director of photography at the Museum of Modern Art (MoMA), John Szarkowski remarked in *William Eggleston's Guide*, "While editing directly from life, photographers have found it too difficult to see simultaneously both the blue and the sky." Szarkowski insightfully revealed a notable gap between general and aesthetic visual understanding: while the former emphasizes identifying factual elements in an image (the sky), the latter transcends mere object identification, viewing it instead as an aesthetic component--a pure expanse of blue, valued purely as a color block in visual aesthetics. Such distinctions between general visual understanding (detection, localization, etc.) and aesthetic perception (color, lighting, composition, etc.) pose a significant challenge for existing Multimodal Large Language Models (MLLMs) in comprehending image aesthetics, which is increasingly needed in real-world applications, from image recommendation and enhancement to generation. To fundamentally advance the aesthetic understanding of MLLMs, we introduce a novel dataset, PhotoCritique, derived from extensive discussions among professional photographers and enthusiasts, distinguished by its large scale, expertise, and diversity. Additionally, we propose a new model, PhotoEye, an MLLM featuring a language-guided multi-view vision fusion mechanism for understanding image aesthetics from multiple perspectives. Finally, we introduce PhotoBench, a comprehensive and professional benchmark for aesthetic visual understanding. Our model demonstrates significant advantages over both open-source and commercial models on existing benchmarks and PhotoBench.more » « less
-
This work introduces a transformer-based image and video tokenizer leveraging Binary Spherical Quantization (BSQ). The method projects high-dimensional visual embeddings onto a lower-dimensional hypersphere followed by binary quantization. BSQ offers three key benefits: (1) parameter efficiency without requiring an explicit codebook, (2) scalability to arbitrary token dimensions, and (3) high compression capability—up to 100× compression of visual data with minimal distortion. The tokenizer architecture includes a transformer encoder-decoder with block-wise causal masking to handle variable-length video inputs. The resulting model, BSQ-ViT, achieves state-of-the-art visual reconstruction performance on image and video benchmarks while delivering 2.4× higher throughput compared to previous best methods. Additionally, BSQ-ViT supports video compression via autoregressive priors for adaptive arithmetic coding, achieving results comparable to leading video compression standards. Furthermore, it enables masked language models to achieve competitive image synthesis quality relative to GAN- and diffusion-based approaches.more » « less
-
Abstract State-of-the-art quantum machine learning (QML) algorithms fail to offer practical advantages over their notoriously powerful classical counterparts, due to the limited learning capabilities of QML algorithms, the constrained computational resources available on today’s noisy intermediate-scale quantum (NISQ) devices, and the empirically designed circuit ansatz for QML models. In this work, we address these challenges by proposing a hybrid quantum–classical neural network (CaNN), which we call QCLIP, for Quantum Contrastive Language-Image Pre-Training. Rather than training a supervised QML model to predict human annotations, QCLIP focuses on more practical transferable visual representation learning, where the developed model can be generalized to work on unseen downstream datasets. QCLIP is implemented by using CaNNs to generate low-dimensional data feature embeddings followed by quantum neural networks to adapt and generalize the learned representation in the quantum Hilbert space. Experimental results show that the hybrid QCLIP model can be efficiently trained for representation learning. We evaluate the representation transfer capability of QCLIP against the classical Contrastive Language-Image Pre-Training model on various datasets. Simulation results and real-device results on NISQIBM_Aucklandquantum computer both show that the proposed QCLIP model outperforms the classical CLIP model in all test cases. As the field of QML on NISQ devices is continually evolving, we anticipate that this work will serve as a valuable foundation for future research and advancements in this promising area.more » « less
-
Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an image from a noisy or corrupted measurement of that image. To achieve state-of-the-art performance, training on large and diverse sets of images is considered critical. However, it is often difficult and/or expensive to collect large amounts of training images. Inspired by the success of Data Augmentation (DA) for classification problems, in this paper, we propose a pipeline for data augmentation for accelerated MRI reconstruction and study its effectiveness at reducing the required training data in a variety of settings. Our DA pipeline, MRAugment, is specifically designed to utilize the invariances present in medical imaging measurements as naive DA strategies that neglect the physics of the problem fail. Through extensive studies on multiple datasets we demonstrate that in the low-data regime DA prevents overfitting and can match or even surpass the state of the art while using significantly fewer training data, whereas in the high-data regime it has diminishing returns. Furthermore, our findings show that DA can improve the robustness of the model against various shifts in the test distribution.more » « less
