Jinan Sun

Haixin Wang, Jianlong Chang, Xiao Luo et al.

Despite recent competitive performance across a range of vision tasks, vision Transformers still have an issue of heavy computational costs. Recently, vision prompt learning has provided an economic solution to this problem without fine-tuning the whole large-scale models. However, the efficiency of existing models are still far from satisfactory due to insertion of extensive prompts blocks and trick prompt designs. In this paper, we propose an efficient vision model named impLicit vIsion prOmpt tuNing (LION), which is motivated by deep implicit models with stable memory costs for various complex tasks. In particular, we merely insect two equilibrium implicit layers in two ends of the pre-trained main backbone with parameters in the backbone frozen. Moreover, we prune the parameters in these two layers according to lottery hypothesis. The performance obtained by our LION are promising on a wide range of datasets. In particular, our LION reduces up to 11.5% of training parameter numbers while obtaining higher performance compared with the state-of-the-art baseline VPT, especially under challenging scenes. Furthermore, we find that our proposed LION had a good generalization performance, making it an easy way to boost transfer learning in the future.

Yihang Zhai, Haixin Wang, Jianlong Chang et al.

Instruction tuning has shown promising potential for developing general-purpose AI capabilities by using large-scale pre-trained models and boosts growing research to integrate multimodal information for creative applications. However, existing works still face two main limitations: the high training costs and heavy computing resource dependence of full model fine-tuning, and the lack of semantic information in instructions, which hinders multimodal alignment. Addressing these challenges, this paper proposes a novel approach to utilize Parameter-Efficient Tuning for generAl-purpose vision-Language models, namely PETAL. PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique, which significantly reduces the training costs and reliance on heavy computing resources. Furthermore, PETAL enhances the semantic depth of instructions in two innovative ways: 1) by introducing adaptive instruction mixture-of-experts(MOEs), and 2) by fortifying the score-based linkage between parameter-efficient tuning and mutual information. Our extensive experiments across five multimodal downstream benchmarks reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness. Additionally, our approach demonstrates remarkable advantages in few-shot settings, backed by comprehensive visualization analyses. Our source code is available at: https://github. com/melonking32/PETAL.

Haixin Wang, Xinlong Yang, Jianlong Chang et al.

Driven by the progress of large-scale pre-training, parameter-efficient transfer learning has gained immense popularity across different subfields of Artificial Intelligence. The core is to adapt the model to downstream tasks with only a small set of parameters. Recently, researchers have leveraged such proven techniques in multimodal tasks and achieve promising results. However, two critical issues remain unresolved: how to further reduce the complexity with lightweight design and how to boost alignment between modalities under extremely low parameters. In this paper, we propose A graceful prompt framework for cross-modal transfer (Aurora) to overcome these challenges. Considering the redundancy in existing architectures, we first utilize the mode approximation to generate 0.1M trainable parameters to implement the multimodal prompt tuning, which explores the low intrinsic dimension with only 0.04% parameters of the pre-trained model. Then, for better modality alignment, we propose the Informative Context Enhancement and Gated Query Transformation module under extremely few parameters scenes. A thorough evaluation on six cross-modal benchmarks shows that it not only outperforms the state-of-the-art but even outperforms the full fine-tuning approach. Our code is available at: https://github.com/WillDreamer/Aurora.

Jianlong Chang, Haixin Wang, Zhiyuan Dang et al.

The pursuit of artificial general intelligence continuously demands generalization in one model across myriad tasks, even those not seen before. However, current AI models are isolated from each other for being limited to specific tasks, now first defined as Intelligence Islands. To unify Intelligence Islands into one, we propose Pangaea, the first AI supercontinent akin to the geological Pangaea. Pangaea encodes any data into a unified format and accumulates universal knowledge through pre-training on 296 datasets across diverse modalities. Eventually, it demonstrates remarkable generalization across 45 general tasks and 15 scientific tasks encompassing a wide range of scientific subjects. By investigating Pangaea deeper, the scaling effect of modality is revealed, quantifying the universal knowledge accumulation across modalities as the cumulative distribution function of a geometric distribution. On the whole, Pangaea shows strong potential to handle myriad tasks, indicating a new direction toward artificial general intelligence.

Chaoya Jiang, Rui Xie, Wei Ye et al.

Cross-modal contrastive learning in vision language pretraining (VLP) faces the challenge of (partial) false negatives. In this paper, we study this problem from the perspective of Mutual Information (MI) optimization. It is common sense that InfoNCE loss used in contrastive learning will maximize the lower bound of MI between anchors and their positives, while we theoretically prove that MI involving negatives also matters when noises commonly exist. Guided by a more general lower bound form for optimization, we propose a contrastive learning strategy regulated by progressively refined cross-modal similarity, to more accurately optimize MI between an image/text anchor and its negative texts/images instead of improperly minimizing it. Our method performs competitively on four downstream cross-modal tasks and systematically balances the beneficial and harmful effects of (partial) false negative samples under theoretical guidance.

Tong Zhang, Wei Ye, Baosong Yang et al.

Low-frequency word prediction remains a challenge in modern neural machine translation (NMT) systems. Recent adaptive training methods promote the output of infrequent words by emphasizing their weights in the overall training objectives. Despite the improved recall of low-frequency words, their prediction precision is unexpectedly hindered by the adaptive objectives. Inspired by the observation that low-frequency words form a more compact embedding space, we tackle this challenge from a representation learning perspective. Specifically, we propose a frequency-aware token-level contrastive learning method, in which the hidden state of each decoding step is pushed away from the counterparts of other target words, in a soft contrastive way based on the corresponding word frequencies. We conduct experiments on widely used NIST Chinese-English and WMT14 English-German translation tasks. Empirical results show that our proposed methods can not only significantly improve the translation quality but also enhance lexical diversity and optimize word representation space. Further investigation reveals that, comparing with related adaptive training strategies, the superiority of our method on low-frequency word prediction lies in the robustness of token-level recall across different frequencies without sacrificing precision.