Intel / tvp-base

TVP base model

The TVP model was proposed in Text-Visual Prompting for Efficient 2D Temporal Video Grounding by Yimeng Zhang, Xin Chen, Jinghan Jia, Sijia Liu, Ke Ding. The goal of this model is to incorporate trainable prompts into both visual inputs and textual features to temporal video grounding(TVG) problems. It was introduced in this paper .

Factors

Relevant factors: The model's performance may vary across different video content, such as variations in video quality, lighting conditions, or genres (e.g., action vs. dialogue-heavy scenes). Evaluation factors: Performance has been evaluated on benchmark datasets like Charades-STA and ActivityNet Captions, focusing on metrics relevant to temporal video grounding accuracy.

Metrics

Model performance measures: The model employs metrics such as the Temporal-Distance IoU (TDIoU) loss for efficient learning and performance evaluation in TVG tasks.

Experiments on two benchmark datasets, Charades-STA and ActivityNet Captions datasets, empirically show that the proposed TVP significantly boosts the performance of 2D TVG (e.g., 9.79% improvement on Charades-STA and 30.77% improvement on ActivityNet Captions) and achieves 5× inference acceleration over TVG using 3D visual features.

Training Data

The TVP model was pretrained on public datasets such as Charades.

Charades is dataset composed of 9848 videos of daily indoors activities collected through Amazon Mechanical Turk. 267 different users were presented with a sentence, that includes objects and actions from a fixed vocabulary, and they recorded a video acting out the sentence (like in a game of Charades). The dataset contains 66,500 temporal annotations for 157 action classes, 41,104 labels for 46 object classes, and 27,847 textual descriptions of the videos. This work was presented at ECCV2016.

Each video has been exhaustively annotated using consensus from 4 workers on the training set, and from 8 workers on the test set. Please refer to the updated accompanying publication for details. Please contact [email protected] for questions about the dataset.

Quantitative Analyses

Unitary results: Refer to Table 2 in the provided paper for TVP's performance on the Temporal Video Grounding task.

How to use

Here is how to use this model to get the logits of a given video and text in PyTorch:

import av
import cv2
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoProcessor, TvpForVideoGrounding


def pyav_decode(container, sampling_rate, num_frames, clip_idx, num_clips, target_fps):
    '''
    Convert the video from its original fps to the target_fps and decode the video with PyAV decoder.
    Returns:
        frames (tensor): decoded frames from the video. Return None if the no
            video stream was found.
        fps (float): the number of frames per second of the video.
    '''
    fps = float(container.streams.video[0].average_rate)
    clip_size = sampling_rate * num_frames / target_fps * fps
    delta = max(container.streams.video[0].frames - clip_size, 0)
    start_idx = delta * clip_idx / num_clips
    end_idx = start_idx + clip_size - 1
    timebase = container.streams.video[0].duration / container.streams.video[0].frames
    video_start_pts = int(start_idx * timebase)
    video_end_pts = int(end_idx * timebase)
    stream_name = {"video": 0}
    seek_offset = max(video_start_pts - 1024, 0)
    container.seek(seek_offset, any_frame=False, backward=True, stream=container.streams.video[0])
    frames = {}
    for frame in container.decode(**stream_name):
        if frame.pts < video_start_pts:
            continue
        if frame.pts <= video_end_pts:
            frames[frame.pts] = frame
        else:
            frames[frame.pts] = frame
            break
    frames = [frames[pts] for pts in sorted(frames)]
    return frames, fps


def decode(container, sampling_rate, num_frames, clip_idx, num_clips, target_fps):
    '''
    Decode the video and perform temporal sampling.
    Args:
        container (container): pyav container.
        sampling_rate (int): frame sampling rate (interval between two sampled frames).
        num_frames (int): number of frames to sample.
        clip_idx (int): if clip_idx is -1, perform random temporal sampling.
            If clip_idx is larger than -1, uniformly split the video to num_clips
            clips, and select the clip_idx-th video clip.
        num_clips (int): overall number of clips to uniformly sample from the given video.
        target_fps (int): the input video may have different fps, convert it to
            the target video fps before frame sampling.
    Returns:
        frames (tensor): decoded frames from the video.
    '''
    assert clip_idx >= -2, "Not a valied clip_idx {}".format(clip_idx)
    frames, fps = pyav_decode(container, sampling_rate, num_frames, clip_idx, num_clips, target_fps)
    clip_size = sampling_rate * num_frames / target_fps * fps
    index = torch.linspace(0, clip_size - 1, num_frames)
    index = torch.clamp(index, 0, len(frames) - 1).long().tolist()
    frames = [frames[idx] for idx in index]
    frames = [frame.to_rgb().to_ndarray() for frame in frames]
    frames = torch.from_numpy(np.stack(frames))
    return frames

def get_resize_size(image, max_size):
    '''
    Args:
        image: np.ndarray
        max_size: The max size of height and width
    Returns:
        (height, width)
    Note the height/width order difference >>> pil_img = Image.open("raw_img_tensor.jpg") >>> pil_img.size (640,
    480) # (width, height) >>> np_img = np.array(pil_img) >>> np_img.shape (480, 640, 3) # (height, width, 3)
    '''
    height, width = image.shape[-2:]
    if height >= width:
        ratio = width * 1.0 / height
        new_height = max_size
        new_width = new_height * ratio
    else:
        ratio = height * 1.0 / width
        new_width = max_size
        new_height = new_width * ratio
    size = {"height": int(new_height), "width": int(new_width)}
    return size

file = hf_hub_download(repo_id="Intel/tvp_demo", filename="AK2KG.mp4", repo_type="dataset")
model = TvpForVideoGrounding.from_pretrained("Intel/tvp-base")

decoder_kwargs = dict(
    container=av.open(file, metadata_errors="ignore"),
    sampling_rate=1,
    num_frames=model.config.num_frames,
    clip_idx=0,
    num_clips=1,
    target_fps=3,
)
raw_sampled_frms = decode(**decoder_kwargs).permute(0, 3, 1, 2)

text = "a person is sitting on a bed."
processor = AutoProcessor.from_pretrained("Intel/tvp-base")
size = get_resize_size(raw_sampled_frms, model.config.max_img_size)
model_inputs = processor(
    text=[text], videos=list(raw_sampled_frms.numpy()), return_tensors="pt", max_text_length=100, size=size
)

model_inputs["pixel_values"] = model_inputs["pixel_values"].to(model.dtype)
model_inputs["labels"] = torch.tensor([18.1, 0.0, 6.8])
output = model(**model_inputs)
print(f"The model's output is {output}")

def get_video_duration(filename):
    cap = cv2.VideoCapture(filename)
    if cap.isOpened():
        rate = cap.get(5)
        frame_num = cap.get(7)
        duration = frame_num/rate
        return duration
    return -1

duration = get_video_duration(file)
timestamp = output['logits'].tolist()
start, end = round(timestamp[0][0]*duration, 1), round(timestamp[0][1]*duration, 1)
print(f"The time slot of the video corresponding to the text \"{text}\" is from {start}s to {end}s")

BibTeX entry and citation info

@inproceedings{zhang2023text,
  title={Text-visual prompting for efficient 2d temporal video grounding},
  author={Zhang, Yimeng and Chen, Xin and Jia, Jinghan and Liu, Sijia and Ding, Ke},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={14794--14804},
  year={2023}
}

Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.