VALSE 论文速览 第140期：通过纯文本训练解码CLIP隐空间的零样本描述方法 ...

论文题目：通过纯文本训练解码CLIP隐空间的零样本描述方法

作者列表：

Wei Li (Zhejiang University), Linchao Zhu (Zhejiang University), Longyin Wen (ByteDance Inc), Yi Yang (Zhejiang University)

B站观看网址：

https://www.bilibili.com/video/BV1V84y127Bq/

论文摘要：

Large-scale pre-trained multi-modal models (e.g., CLIP) demonstrate strong zeroshot transfer capability in many discriminative tasks, e.g., image classification. Their adaptation to zero-shot image-conditioned text generation tasks has drawn increasing interest. Prior arts approach to zero-shot captioning by either utilizing the existing large language models (e.g., GPT-2) or pre-training the encoder-decoder network in an end-to-end manner. However, the large language models may not generate sensible descriptions due to the task discrepancy between captioning and language modeling, while the end-to-end pre-training requires paired data and extensive computational resources. In this work, we propose a simple framework, named DeCap, for zero-shot captioning. We introduce a lightweight visual-aware language decoder. This decoder is both data-efficient and computation-efficient: 1) it only requires the text data for training, easing the burden on the collection of paired data. 2) it does not require end-to-end training. When trained with text-only data, the decoder takes the text embedding extracted from the off-the-shelf CLIP encoder as a prefix embedding. The challenge is that the decoder is trained on the text corpus but at the inference stage, it needs to generate captions based on visual inputs. Though the CLIP text embedding and the visual embedding are correlated, the modality gap issue is widely observed in multi-modal contrastive models that prevents us from directly taking the visual embedding as the prefix embedding. We propose a training-free mechanism to reduce the modality gap. We project the visual embedding into the CLIP text embedding space, while the projected embedding retains the information of the visual input. Taking the projected embedding as the prefix embedding, the decoder generates high-quality descriptions that match the visual input. The experiments show that DeCap outperforms other zero-shot captioning methods and unpaired captioning methods by a large margin on the typical image captioning benchmarks, i.e., MSCOCO and NoCaps. We apply DeCap to video captioning and achieve stateof-the-art zero-shot performance on MSR-VTT and ActivityNet-Captions.

论文信息：

[1] Wei Li, Linchao Zhu, Longyin wen and Yi Yang. DeCap: Decoding CLIP Latents for Zero-Shot Captioning via Text-Only Training. ICLR 2023

论文链接：

[https://arxiv.org/pdf/2303.03032.pdf]

代码链接：

[https://github.com/dhg-wei/DeCap]

视频讲者简介：

李威，浙江大学计算机学院博士生，主要研究方向是视觉语言多模态。

特别鸣谢本次论文速览主要组织者：

月度轮值AC：杨旭 (西安电子科技大学)

季度轮值AC：叶茫 (武汉大学)