2.2.2 Model Adaptation by Learning Prompts 这里的目标是引导预训练的CLIP模型以最少的训练来执行各种视频任务。作者通过在文本token中添加连续随机向量 (“提示向量”) 序列来实现有效的模型适应。在训练过程中,CLIP的图像和文本编码器都被冻结,梯度将流经文本编码器,仅更新提示向量。最终,这些学习的向量最终构造了...
This enables the video network to benefit from the pretrained image model. However, this requires substantial computation and memory resources for finetuning on videos and the alternative of directly using pretrained image features without finetuning the image backbone leads to subpar results. ...
)model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")inputs = processor(text=text, return_tensors="pt", padding=True)for tensor in tensors: image_tensor = torch.load(tensor) inputs['pixel_values'] = image_tensor outputs = model(**inputs)然后访问模型...
本文的框架如上图所示,作者的目标是有效地引导基于图像的时间语言模型来处理新的下游任务,这个过程称之为模型适应(model adaptation)。 2.1. Visual-Language Model: CLIP 给定一个采样batch中的N个对 (图像,文本),分别使用两个编码器计...
A CLIP-Enhanced Method for Video-Language Understanding 论文地址:https://arxiv.org/abs/2110.07137 代码地址:未开源 2. Motivation 视频语言理解越来越受到研究界的关注。最近,NeurIPS2021上提出了视频和语言理解评估(VALUE)基准,这是一个由3类任务(VideoQA, Retrieval, Captioning)和11个数据集组成的统一基准。
definsert_video_scene(videoID,sceneIds):b=",".join(sceneIds)level_instance=leveldb.LevelDB('./dbs/scene_index')level_instance.Put(videoID.encode('utf-8'),b.encode('utf-8'))#...scene_ids=[]forfinscene_clip_embeddings:#..asshowninprevious step scene_ids.append(scene_id)scene_embeddi...
def video_preprocessing(video_path, fnum=8): video = cv2.VideoCapture(video_path) frames = [x for x in _frame_from_video(video)] step = len(frames) // fnum frames = frames[::step][:fnum] vid_tube = [] for fr in frames: ...
str: distribution strategy, currently either slurm or none oc_model_name: str: open_clip model name, used for selecting CLIP architecture pretrained: str: open_clip pretrained weights name POSITIONAL ARGUMENTS SRC FLAGS --dest=DEST Default: '' --output_format=OUTPUT_FORMAT Default: 'files' --...
model that has been fine-tuned for video-language tasks with the powerful pre-trained CLIP can be effectively transferred to a small student only at the fine-tuning stage. Especially, a new layer-wise alignment with the student as the base is proposed for knowledge distillation of the ...
是因为在同样计算开销的情况下,video pre-train能见到的data diversity 太小了。除此之外,更让人感兴趣的是,到底需不需要用video 来pretrain model, 如果需要,什么样的task才需要。 相比较去self-supervised的去训练个video模型来说,比如,MIL-NCE, CoCLR, BYOL, 这种image+temporal的形式,其实是要节省很多算力的...