40 research outputs found
Chain of Thought Prompt Tuning in Vision Language Models
Language-Image Pre-training has demonstrated promising results on zero-shot
and few-shot downstream tasks by prompting visual models with natural language
prompts. However, most recent studies only use a single prompt for tuning,
neglecting the inherent step-to-step cognitive reasoning process that humans
conduct in complex task settings, for example, when processing images from
unfamiliar domains. Chain of Thought is a simple and effective approximation to
human reasoning process and has been proven useful for natural language
processing (NLP) tasks. Based on this cognitive intuition, we believe that
conducting effective reasoning is also an important problem in visual tasks,
and a chain of thought could be a solution to this problem. In this work, we
propose a novel chain of thought prompt tuning for vision-language modeling.
Extensive experiments show that our method not only generalizes better in image
classification tasks, has greater transferability beyond a single dataset, and
has stronger domain generalization performance, but also performs much better
in imagetext retrieval and visual question answering, which require more
reasoning capabilities. We are the first to successfully adapt chain-of-thought
prompting that combines visual and textual embeddings. We will release our
code
Exemplar-Based Sketch Colorization with Cross-Domain Dense Semantic Correspondence
This paper aims to solve the task of coloring a sketch image given a ready-colored exemplar image. Conventional exemplar-based colorization methods tend to transfer styles from reference images to grayscale images by employing image analogy techniques or establishing semantic correspondences. However, their practical capabilities are limited when semantic correspondences are elusive. This is the case with coloring for sketches (where semantic correspondences are challenging to find) since it contains only edge information of the object and usually contains much noise. To address this, we present a framework for exemplar-based sketch colorization tasks that synthesizes colored images from sketch input and reference input in a distinct domain. Generally, we jointly proposed our domain alignment network, where the dense semantic correspondence can be established, with a simple but valuable adversarial strategy, that we term the structural and colorific conditions. Furthermore, we proposed to utilize a self-attention mechanism for style transfer from exemplar to sketch. It facilitates the establishment of dense semantic correspondence, which we term the spatially corresponding semantic transfer module. We demonstrate the effectiveness of our proposed method in several sketch-related translation tasks via quantitative and qualitative evaluation
Exemplar-Based Sketch Colorization with Cross-Domain Dense Semantic Correspondence
This paper aims to solve the task of coloring a sketch image given a ready-colored exemplar image. Conventional exemplar-based colorization methods tend to transfer styles from reference images to grayscale images by employing image analogy techniques or establishing semantic correspondences. However, their practical capabilities are limited when semantic correspondences are elusive. This is the case with coloring for sketches (where semantic correspondences are challenging to find) since it contains only edge information of the object and usually contains much noise. To address this, we present a framework for exemplar-based sketch colorization tasks that synthesizes colored images from sketch input and reference input in a distinct domain. Generally, we jointly proposed our domain alignment network, where the dense semantic correspondence can be established, with a simple but valuable adversarial strategy, that we term the structural and colorific conditions. Furthermore, we proposed to utilize a self-attention mechanism for style transfer from exemplar to sketch. It facilitates the establishment of dense semantic correspondence, which we term the spatially corresponding semantic transfer module. We demonstrate the effectiveness of our proposed method in several sketch-related translation tasks via quantitative and qualitative evaluation