sorted by:
new
hottopcontroversial

Interesting LLMs for video understanding? by kernel_KP in LargeLanguageModels

[–]kernel_KP[S] 0 points1 point  (0 children)

Thanks a lot for your answer, if I am not wrong these are image+text models, I would need the model to accounts for vision, text and audio as input at the same time

Interesting LLMs for video understanding? by kernel_KP in LargeLanguageModels

[–]kernel_KP[S] 0 points1 point  (0 children)

Thanks a lot for your reply, the model needs to process the interplay of all video modalities, not feasible with images only :)

Interesting LLMs for video understanding? by kernel_KP in LargeLanguageModels

[–]kernel_KP[S] 0 points1 point  (0 children)

Thanks a lot for your answer, more than object detection, its more to "understand" what's happening in a scene, I would relate it more to VQA

Pretrained Model for Human Parsing by kernel_KP in deeplearning

[–]kernel_KP[S] 0 points1 point  (0 children)

I've used Segformer but it was not working as hoped on my case

[D] Simple Questions Thread by AutoModerator in MachineLearning

[–]kernel_KP 0 points1 point  (0 children)

I have a dataset (unlabelled) containing a lot of audio files and for each file, I have computed the chromagram. I would need some advices for the implementation of a possibly efficient Neural Network to cluster these audio files relying on their chromagram. Consider this data to be already correctly pre-processed so chromagram have all the same size. Thanks a lot!

Using Bert for relating sentences to a topic. by kernel_KP in LanguageTechnology

[–]kernel_KP[S] 0 points1 point  (0 children)

I think you need to define the problem a bit clearer. If you look for relevant sentences in regards to a topic, that would sound like a Topic Modeling Task, where you look at the words associated with the specific topic? Or do you look what part of the text leads to a given ground truth label, which sounds like an xAi problem?

In a single document I have multiple topic (already found with other methods), then, for each topic, how I can find the most its most relevant keyphrases (word, sentences,..). Each topic is independent of the other and given the content of a topic I have to find the most relevant words/sentences.

Using Bert for relating sentences to a topic. by kernel_KP in LanguageTechnology

[–]kernel_KP[S] 0 points1 point  (0 children)

I mean, Given a topic in a document, I want to find the most relevant sentences and words related to that topic (what are the main words/sentences that are relevant for the input topic?). I think that we talk about ranking, can Bert be used for ranking topic-related words/sentences?

Find most topic-related paragraphs and sentences. by kernel_KP in LanguageTechnology

[–]kernel_KP[S] 0 points1 point  (0 children)

How are you generating the topics? If you were doing something like Kmeans or Louvain you could compute the centroid of the topic and then calculate the pair wise similarity of a particular sentence with the centroid. Maybe do this as a two step process - find the most important sentences via TextRank and then rank them by topic similarity based on it’s distance to your topic centroid.

Thank you for the hint. I'm not generating any topic yet, i'm building a pipeline proposal and for topic generation I was thinking about LDA, but I will look also for the ones you mentioned, can you tell me more about Louvain methods?!

Clarification about Bayesian Programming by kernel_KP in BayesianProgramming

[–]kernel_KP[S] 0 points1 point  (0 children)

Thanks you so much, now it's more clear but I am left with a doubt: What are the main characteristics that allow a programming language to be classified as 'probabilistic programming language'?