From 74bec7df48504ac03464927a9f32a3bdf762f1a5 Mon Sep 17 00:00:00 2001 From: Gasp34 Date: Mon, 30 Sep 2024 10:51:41 +0200 Subject: [PATCH 1/7] add images --- .../_posts/2024-09-03-Eyes_wide_shut.md | 67 ++++++++++++++++++ collections/images/EWS/EWS1.jpg | Bin 0 -> 747929 bytes collections/images/EWS/EWS2.jpg | Bin 0 -> 256011 bytes collections/images/EWS/EWS3.jpg | Bin 0 -> 275854 bytes collections/images/EWS/EWS4.jpg | Bin 0 -> 59086 bytes collections/images/EWS/EWS5.jpg | Bin 0 -> 273722 bytes collections/images/EWS/EWS6.jpg | Bin 0 -> 59872 bytes collections/images/EWS/EWS7.jpg | Bin 0 -> 185299 bytes collections/images/EWS/EWS_T1.jpg | Bin 0 -> 432277 bytes collections/images/EWS/EWS_T2.jpg | Bin 0 -> 95829 bytes collections/images/EWS/EWS_T3.jpg | Bin 0 -> 90661 bytes 11 files changed, 67 insertions(+) create mode 100755 collections/_posts/2024-09-03-Eyes_wide_shut.md create mode 100644 collections/images/EWS/EWS1.jpg create mode 100644 collections/images/EWS/EWS2.jpg create mode 100644 collections/images/EWS/EWS3.jpg create mode 100644 collections/images/EWS/EWS4.jpg create mode 100644 collections/images/EWS/EWS5.jpg create mode 100644 collections/images/EWS/EWS6.jpg create mode 100644 collections/images/EWS/EWS7.jpg create mode 100644 collections/images/EWS/EWS_T1.jpg create mode 100644 collections/images/EWS/EWS_T2.jpg create mode 100644 collections/images/EWS/EWS_T3.jpg diff --git a/collections/_posts/2024-09-03-Eyes_wide_shut.md b/collections/_posts/2024-09-03-Eyes_wide_shut.md new file mode 100755 index 00000000..1dfb3abb --- /dev/null +++ b/collections/_posts/2024-09-03-Eyes_wide_shut.md @@ -0,0 +1,67 @@ +--- +layout: review +title: "Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs" +tags: VLM, CLIP, MLLM, contrastive-learning +author: "Gaspard Dussert" +cite: + authors: "Shengbang Tong, Zhuang Liu, Yuexiang Zhai, Yi Ma, Yann LeCun, Saining Xie" + title: "Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs" + venue: "CVPR 2024" +pdf: "https://openaccess.thecvf.com/content/CVPR2024/html/Tong_Eyes_Wide_Shut_Exploring_the_Visual_Shortcomings_of_Multimodal_LLMs_CVPR_2024_paper.html" +--- + +# Highlights + +* + +# Introduction + +
+

Figure 1.

+ +
+

Figure 2.

+ +
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Figure 3.

+ +
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Figure 4.

+ +
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Figure 5.

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Figure 6.

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Figure 7.

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+

Table 1.

+ +
+

Table 2.

+ +
+

Table 3.

+ + +## Mask decoder + +The decoder architecture is again quite similar to that of SAM. It takes as input the conditioned frame embeddings (with potential mask encoded embeddings) on one hand, and sparse prompts tokens, concatenated with tokens used for the output, on the other hand. The decoder stacks transformer blocks with self-attention between prompt+ouput tokens and alternated "image to token" and "token to image" cross-attention. + +> N.B: X to Y attention means that X is query and Y is key and value in the attention layer. + +The model produces multiple from the resulting tokens : +* The tokens are upsampled with convolutions and summed with stages 1 and 2 frame features from Hiera image encoder. They are multiplied with the output tokens (after passing through 3 MLP layers) to produce **low-resolution masks** (at a resolution 4x lower than input image). +* The output tokens is also used to produce what are called **object pointers**, which are lightweight vectors to encode high-level semantic information of the objecct to segment. +* As in SAM, there could be ambiguity on the mask to predict, as an object can have several subparts that are also segmented. For this reason, the model produces multiple output masks (3 in general) and predicts the **IoU scores** between the ground truth and each mask. +* Finally, a specific constraint of video segmentation is that objects of interest can disappear from the image, momentarily or permanently. Hence, SAM 2 also predicts an **oclusion score** for each object. + +From these outputs, few post-processing steps, including resolving mask covering (based on the predicted IoU), mask refinement and a final 4x upsampling, allow to produce the final segmentation map at the original image size. + +# Reference + +[^1]: C. Ryali, Y.-T. Hu, D. Bolya, C. Wei, H. Fan, P.-Y. Huang, V Aggarwal, A. Chowdhury, O. Poursaeed, J. Hoffman, J. Malik, Y. Li, and C. Feichtenhofer. Hiera: A hierarchical vision transformer without the bells-and-whistles. International Conference on Machine Learning (ICML), 2023. + diff --git a/collections/images/EWS/EWS1.jpg b/collections/images/EWS/EWS1.jpg new file mode 100644 index 0000000000000000000000000000000000000000..5c0c6266113a8c0da03be41644f14cb302b3ae69 GIT binary patch literal 747929 zcmd?Q1yo(j@;7)6F2N-s1W0fv5G=U6yAuKgJ-B;BrcUM*)>+xnSU6o-PD9I9NDD1SBLx1VlunXK2rmkWrBl5m7KuP|?uQG0>5oVPavTV?mzL zpNIgTMnc0NKn9{CBO*iI{tvIm?*I%0SPS?KXdngv3Ihm@0eox+5CVVzAS@7a0seKt zLBqhpBR~NWks$51=m5yfP%uz1FeoTUi12V|KqzP!04y9l1_I_AMl618VtGA8CRJ-3 z+~;^C0-qyeVt=bMN#>qjYISgW>!HZ+b`Zx%VeVZnPY25 z>c#JNm9r)IcVE1>pt?`p-N){Rsn^6amtz2w@)x4TOeAgoAxz z4S)uC;t2y5!Ver4zr2EVBsLDQqTc6>il4Yl@0e|l;8_HIER%eUBF!B6Qu&;WRj~Eg z_M4JEn~+@=xv+QJ%Hs^+88l={3}_4hFW_D=9UIC^4hhKT#te_G!5+PGt9YulGMRPb z>EBCAxx{(l(O`$DQ-;=#BVE{8M)`Au;+JlEo}JgDO!zqYB1Q0cnu0L6RvMmM{-@TLAkoyUE<^KNr57Xjau zQ1tM&AM}+#i@5rUJKNI~UA&IdqC(IcoI5f*}sKu zo$^bej{t-pkAQ*Sy8e?+-T%+jwEtgf0=rHNZqF{ixTgQ^iKx0wHDKZnQBj-4Wu^Zp 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