Concept Graph & Resume using Claude 3 Opus | Chat GPT4 | Gemini Adv | Llama 3:

Resume:

1.-Zero-shot learning aims to classify images into unseen classes by leveraging side information like semantic embeddings of labels.

2.-Unsupervised semantic embeddings of labels allow working with many classes without manually annotating attributes.

3.-The approach projects images into the semantic label space and uses nearest neighbor search to classify test images.

4.-Key challenges are defining the semantic label embedding, projecting images into that space, and performing nearest neighbor search.

5.-Skip-gram model is used to learn semantic label embeddings from word co-occurrences in an unsupervised way.

6.-The ConSE (convex combination of semantic embeddings) model projects images by taking a weighted combination of training label embeddings.

7.-Weights are the conditional probabilities of training labels given the image, obtained from a trained probabilistic classifier.

8.-A parameter t selects averaging only the top t label embeddings to reduce noise from tiny probabilities.

9.-ConSE requires no extra training beyond the initial classifier. Output likely stays on the manifold of label embeddings.

10.-Alternative models learn a regression to map images close to their label embedding and far from incorrect ones.

11.-Experiments done on ImageNet with 1000 training labels and 20,000 zero-shot test labels, using 500-D skip-gram Wikipedia embeddings.

12.-ConSE is compared to DeViSE model which learns a linear mapping of images into embedding space via ranking loss.

13.-Three subsets of test labels used: 1-2 hops, 3 hops, and >3 hops away from training labels in ImageNet hierarchy.

14.-Flat hit@k (% of test images with true label in top k predictions) reported, excluding and including training labels.

15.-ConSE with t=10 outperforms t=1 (can capture label ambiguity) and t=1000 (too much noise). Beats DeViSE by 5-15%.

16.-Performance degrades as test labels get farther in hierarchy from training. All methods prefer predicting training labels when included.

17.-Qualitative results show ConSE predicts relevant labels for images of rare categories like sea lions and hammers.

18.-Even failure cases have sensible predictions like vehicle-related classes for a farm machine image.

19.-ConSE performs worse than DeViSE on training labels, but generalizes better to unseen test labels without overfitting.

20.-In summary, ConSE deterministically embeds images using classifier probabilities and semantic label embeddings, outperforming regression-based approaches.

21.-Hierarchical performance metrics are also reported in the paper to measure taxonomic distance of predictions from ground truth.

22.-A questioner points out limitations of using textual embeddings as a proxy for visual similarity.

23.-Visual and textual similarity may not always align well, e.g. Eiffel Tower mapping close to unrelated categories.

24.-Word embeddings are unsupervised and easily trained on Wikipedia, making them convenient despite this limitation.

25.-Future work could explore using classifier confusion matrices to better capture visual similarities between classes.

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