These are some of the most famous and highest-paid celebrities in the world. From left to right, top to bottom:
Justin Bieber (Canadian singer)
Lionel Messi (Argentine soccer player)
Cristiano Ronaldo (Portuguese soccer player)
Elon Musk (South African-born American entrepreneur and businessman)
Kylie Jenner (American media personality and socialite)
Taylor Swift (American singer-songwriter)
Robert Downey Jr. (American actor)
Scarlett Johansson (American actress)
It’s worth noting that this is just a small sample of the many talented and successful celebrities out there, and that fame and wealth are not the only measures of success or importance.
User
their chinese name?
Qwen-VL
Here are the Chinese names of the celebrities mentioned:
Justin Bieber: 贾斯汀·比伯
Lionel Messi: 莱昂内尔·梅西
Cristiano Ronaldo: 克里斯蒂亚诺·罗纳尔多
Elon Musk: 埃隆·马斯克
Kylie Jenner: 凯莉·詹娜
Taylor Swift: 泰勒·斯威夫特
Robert Downey Jr.: 小罗伯特·唐尼
Scarlett Johansson: 斯嘉丽·约翰逊
Note that these are just one possible translation of their names into Chinese, and there may be other variations used in different contexts or regions.
Where is it in the picture? How did you recognize it?
Qwen-VL
The picture shows a busy street at night in New York City, USA. The Empire State Building can be seen in the background, which is a distinctive landmark of New York City. Therefore, I recognized it as New York City.
A rocky crag by the sea,
A place of solitude and mystery.
The water calm and blue,
Reflecting the sky above, anew.
The mountain stands tall and proud,
Its peak reaching towards the clouds.
A symbol of strength and resilience,
Enduring through time’s relentless persistence.
The world around may change and shift,
But this mountain remains steadfast and uplifted.
A reminder to us all,
To stand firm in our own personal call.
So let us take a moment to pause,
And appreciate this natural cause.
For in its beauty and grandeur,
We find inspiration and renewal, forever.
The image is a screenshot of an app displaying a map of Beijing, China. The map is centered on the Summer Palace and Yuyuantan Park. The app provides directions for driving from Yuyuantan Park to the Summer Palace. It offers two options: one takes 22 minutes and the other takes 25 minutes. The app also indicates that there are local plate restrictions on the route.
The current vehicle speed is 150km/h. According to the picture, how should the vehicle operate?
A. To accelerate
B. Slow down
C. Maintain the original speed
D. Parking
Choose an answer and give reasons.
Qwen-VL
B. Slow down
According to the traffic sign in the picture, the maximum speed limit for the current lane is 100 km/h. If the current vehicle speed is 150 km/h, it exceeds the speed limit and should slow down to comply with the traffic regulations.
The image shows two geometric figures, a rectangular prism and a cube, with their dimensions labeled. The problem asks to calculate the surface area and volume of these figures.
For the rectangular prism:
Length (l) = 4 cm
Width (w) = 2 cm
Height (h) = 3 cm
Surface Area (SA) of a rectangular prism = 2lw + 2lh + 2wh
Volume (V) of a rectangular prism = lwh
Substituting the given values:
SA = 2(4cm)(2cm) + 2(4cm)(3cm) + 2(2cm)(3cm)
SA = 16cm² + 24cm² + 12cm²
SA = 52cm²
V = (4cm)(2cm)(3cm)
V = 24cm³
For the cube:
Side length (a) = 3 dm
Surface Area (SA) of a cube = 6a²
Volume (V) of a cube = a³
Substituting the given value:
SA = 6(3dm)²
SA = 6(9dm²)
SA = 54dm²
V = (3dm)³
V = 27dm³
So, the surface area and volume for each figure are as follows:
The image shows a Scratch programming block, which can be converted into Python code as follows:
importrandom# Set up the variablesa=random.randint(1,50)b=random.randint(1,50)answer=a+b# Ask the user for their inputresponse=int(input(f"What is {a} + {b}? "))# Check if the response matches the answerifresponse==answer:print("Correct!")else:print("Incorrect!")
This Python code generates two random numbers between 1 and 50, adds them together to get the answer, and then asks the user for their response. It then checks if the response matches the answer, and prints out a message accordingly.
Spot a data point that stands out in these charts and what that implicates. Then produce a detailed markdown table for all the data shown.
Qwen-VL
The most striking data point in these charts is the high percentage of plastic waste that is mismanaged in the United States, which stands at 4%. This implies that a significant portion of plastic waste in the US is not being properly disposed of or recycled, leading to environmental pollution and potential harm to ecosystems and human health.
Here’s a detailed markdown table for all the data shown:
Region
Landfilled (2019)
Mismanaged (2019)
Incinerated (2019)
Recycled (2019)
World
49%
22%
19%
9%
United States
73%
4%
19%
4%
Europe
44%
6%
38%
12%
Asia (excluding China and India)
39%
34%
19%
8%
Note: The data source for this information is the OECD (Organisation for Economic Co-operation and Development), and the percentages represent the share of plastic waste that is recycled, landfilled, incinerated, and mismanaged in each region in 2019.
Look at this sequence of three shapes. What shape should come as the fourth shape? Explain your reasoning with detailed descriptions of the first shapes.
Qwen-VL
The sequence of shapes is a triangle, square, and pentagon, followed by a question mark. This pattern suggests that the next shape in the sequence should be a hexagon. The pattern appears to be based on the number of sides in each shape, with each subsequent shape having one more side than the previous one. Therefore, the fourth shape should have six sides, making it a hexagon.
The image presents a three-stage training pipeline for the Qwen-VL series, a visual language model. Each stage is represented by a box labeled “QwenLM” and is accompanied by a smaller box indicating the type of data used in that stage.
In Stage 1, the model is pre-trained using image-text pairs. This stage involves learning from a large dataset of images and their corresponding text descriptions to understand the relationship between visual and linguistic information.
Stage 2 is multi-task pre-training with multi-task and interleaved VL data. Here, the model is trained on multiple tasks simultaneously, such as image captioning, visual question answering, and referring expression comprehension. The use of interleaved data helps the model learn to switch between tasks efficiently.
Finally, in Stage 3, the model undergoes supervised finetuning with chat-interleaved VL data. This stage involves refining the model’s performance on specific tasks using labeled data. The chat-interleaved data provides a more conversational context for the model to learn from, enhancing its ability to understand and respond to natural language queries.
Overall, this three-stage training pipeline aims to develop a robust visual language model capable of understanding and generating human-like responses to visual and linguistic inputs.
Please read the text in this image and return the information in the following JSON format (note xxx is placeholder, if the information is not available in the image, put “N/A” instead).
{“class”: xxx, “DLN”: xxx, “DOB”: xxx, “Name”: xxx, “Address”: xxx, “EXP”: xxx, “ISS”: xxX, “SEX”: xxx, “HGT”: xxx, “WGT”: xxx, “EYES”: xxx, “HAIR”: xxX, “DONOR”: xxx}
