This is my reading note for Scaling Laws for Generative Mixed-Modal Language Models. This paper provides a study of scaling raw on dataset size and model size in multimodality settings.
This is my reading note for Tag2Text: Guiding Vision-Language Model via Image Tagging. This paper proposes to add tag recognition to vision language model and shows improved performance.
This is my reading note for MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks. This paper proposes a new multilingual benchmark to test LLM and provides very limited dataset for multimodality. The language distribution is also strange which houses to much on south, Asia. Overall GPT and Palm get the best performance.
Audio-vision modaility model could improve the quality of face tracking (in speech) as well the robustness (when face get occluded) over vision based solutions. This is my reading note on Audio-vision modaility face tracking.
Please follow the steps below to install virtual box on your windows machine.
This my note on how to convert a OnePlus 10 Pro Chinese version (NE2210) to EU version (NE2213). This version doesn’t need root and bootloader is locked, thus all software will work perfectly. This conversion will require good knowledge of Android (adb, fastboot), a Windows machine and some software. IT HAS RISKS OF BRICKING DEVICE.
This my reading note on Zero-Shot Text-to-Image Generation (aka, DALL-E), its extension Hierarchical Text-Conditional Image Generation with CLIP Latents (aka, DALLE-2 or unCLIP) and StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation. DALL-E is a transformer generating image given captions, by autoregressively modeling the text and image tokens as a single stream of data. StoryDALL-E extends DALL-E by generating a sequence of images for a sequence of caption to complete a story.
Pix2seq: A Language Modeling Framework for Object Detection casts object detection as a language modeling task conditioned on the observed pixel inputs. Object descriptions (e.g., bounding boxes and class labels) are expressed as sequences of discrete tokens, and we train a neural network to perceive the image and generate the desired sequence. Our approach is based mainly on the intuition that if a neural network knows about where and what the objects are, we just need to teach it how to read them out. Experiment results are shown in Table 1, which indicates Pix2seq achieves state of art result on coco.
This is my reading note on DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. Given as input just a few (3~5) images of a subject, DreamBooth fine-tune a pretrained text-to-image model such that it learns to bind a unique identifier with that specific subject. Once the subject is embedded in the output domain of the model, the unique identifier can then be used to synthesize fully-novel photorealistic images of the subject contextualized in different scenes. By leveraging the semantic prior embedded in the model with a new autogenous class-specific prior preservation loss, DreamBooth enables synthesizing the subject in diverse scenes, poses, views, and lighting conditions that do not appear in the reference images. (check Figure 1 as an example)