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+    "adee683ca18e.pdf": "Medical Image Analysis",
+    "7efb2e5563f1.pdf": "Image / Video Generation & Editing",
+    "ba42f09225eb.pdf": "Image / Video Generation & Editing",
+    "e921de7785c4.pdf": "Image / Video Generation & Editing",
+    "e1bfaffb963b.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "5aeb426422df.pdf": "Image / Video Generation & Editing",
+    "e27a43b88ba5.pdf": "Image / Video Generation & Editing",
+    "f2a7c2de438c.pdf": "Image / Video Generation & Editing",
+    "21a205094630.pdf": "Image / Video Generation & Editing",
+    "4d9feb88bcf5.pdf": "Image / Video Generation & Editing",
+    "a659439bcdb9.pdf": "Image / Video Generation & Editing",
+    "c832a92da9b2.pdf": "Image / Video Generation & Editing",
+    "64cc7c3789b8.pdf": "Image / Video Generation & Editing",
+    "2d00d3050c01.pdf": "Autonomous Driving / Robotics / Embodied AI",
+    "3d91a95bf38c.pdf": "Others",
+    "b6d5e2db95a8.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "02de031d331d.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "f1b7743ff517.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "ea431cae0f42.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "731b008c7d10.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "c9c4c71cc917.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "dddac1828124.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "e904dadcb642.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "b6f5264340f7.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "50b63eeca3c8.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "5e183e1c97d2.pdf": "3D Vision / Reconstruction / Gaussian Splatting",
+    "9521e9de58b7.pdf": "Others",
+    "70bdbc4c792d.pdf": "Others",
+    "65734f8bb49d.pdf": "Others",
+    "09d4c0a0da65.pdf": "Others",
+    "93068ccacf7d.pdf": "Others",
+    "78af027647c3.pdf": "Others",
+    "02ef1bf17518.pdf": "Others",
+    "42e93f81aeab.pdf": "Others",
+    "aeb4ad007172.pdf": "Others",
+    "948b29b4e4f7.pdf": "Others",
+    "1a43020f6c08.pdf": "Others",
+    "8d4e4ec90341.pdf": "Others",
+    "0bfac94e64fb.pdf": "Others",
+    "8f5a693ae39d.pdf": "Others",
+    "3a39f9384b01.pdf": "Others",
+    "c0479e0b929f.pdf": "Others",
+    "c1b9051daa2e.pdf": "Others",
+    "1a631564da24.pdf": "Others",
+    "04bae7b6fe0b.pdf": "Others",
+    "d81650bc948c.pdf": "Others",
+    "c23c20b3b50b.pdf": "Others",
+    "b0e192d750ee.pdf": "Others",
+    "ff298e521b03.pdf": "Others"
+  },
+  "caption_papers": {
+    "9eac27775c25.pdf": "CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning",
+    "77da4293cd70.pdf": "Asymmetric Idiosyncrasies in Multimodal Models"
+  },
+  "caprl_table_md": "| Pretraining Dataset | InfoVQA | DocVQA | ChartQA | RealWorldQA | MathVista | SEED2Plus | MME RW | MMB | MMStar | MMVet | AI2D | GQA | Average |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| Vanilla | 43.9 | 81.0 | 72.7 | 55.1 | 41.6 | 56.6 | 30.5 | 68.6 | 44.7 | 41.0 | 68.3 | 61.5 | 55.5 |\n| ShareGPT4V-1M | 46.1 | 82.4 | 74.2 | 55.0 | 44.7 | 60.5 | 29.8 | 68.9 | 45.2 | 42.4 | 70.1 | 61.4 | 56.7 |\n| CapRL-ShareGPT4V-1M | 52.1 | 85.9 | 75.2 | 56.3 | 45.6 | 60.0 | 30.9 | 70.9 | 46.7 | 47.5 | 71.4 | 61.7 | 58.7 |\n| DenseFusion-1M | 49.4 | 84.6 | 74.4 | 54.1 | 44.6 | 59.1 | 30.7 | 69.0 | 45.6 | 40.2 | 70.4 | 62.5 | 57.1 |\n| CapRL-DenseFusion-1M | 55.0 | 87.8 | 77.5 | 56.2 | 44.7 | 62.8 | 32.0 | 71.0 | 46.6 | 49.9 | 72.7 | 62.3 | 59.9 |",
+  "caption_table2_info": {
+    "9eac27775c25.pdf": {
+      "title": "CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning",
+      "table_caption_keywords": [
+        "Ablation",
+        "image sources"
+      ],
+      "required_columns": [
+        "InfoVQA",
+        "DocVQA",
+        "ChartQA",
+        "MathVista",
+        "Average"
+      ],
+      "required_rows": [
+        "CapRL-ShareGPT4V-1M",
+        "CapRL-DenseFusion-1M",
+        "ShareGPT4V-1M",
+        "DenseFusion-1M",
+        "Vanilla"
+      ],
+      "data_row_count": 5,
+      "key_cells": [
+        {
+          "row": "CapRL-ShareGPT4V-1M",
+          "col": "Average",
+          "value": "58.7"
+        },
+        {
+          "row": "CapRL-DenseFusion-1M",
+          "col": "Average",
+          "value": "59.9"
+        },
+        {
+          "row": "CapRL-ShareGPT4V-1M",
+          "col": "InfoVQA",
+          "value": "52.1"
+        },
+        {
+          "row": "CapRL-DenseFusion-1M",
+          "col": "DocVQA",
+          "value": "87.8"
+        },
+        {
+          "row": "ShareGPT4V-1M",
+          "col": "Average",
+          "value": "56.7"
+        }
+      ]
+    },
+    "77da4293cd70.pdf": {
+      "title": "Asymmetric Idiosyncrasies in Multimodal Models",
+      "table_caption_keywords": [
+        "TF-IDF",
+        "phrases",
+        "captioning model"
+      ],
+      "required_columns": [
+        "Claude-3.5-Sonnet",
+        "Gemini-1.5-Pro",
+        "GPT-4o",
+        "Qwen3-VL"
+      ],
+      "required_rows": [],
+      "data_row_count": 10,
+      "key_cells": [
+        {
+          "row": "1",
+          "col": "Claude-3.5-Sonnet",
+          "value": "lighting suggests"
+        },
+        {
+          "row": "1",
+          "col": "GPT-4o",
+          "value": "image depicts"
+        },
+        {
+          "row": "2",
+          "col": "Gemini-1.5-Pro",
+          "value": "low resolution"
+        },
+        {
+          "row": "5",
+          "col": "Qwen3-VL",
+          "value": "depth field"
+        }
+      ]
+    }
+  }
+}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/1.tex

@@ -0,0 +1,39 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{\textwidth}{!}{
+        \begin{tabular}{l|cccccc}
+        \toprule
+        \multirow{2}{*}{\textbf{Model}} & \multicolumn{5}{c}{\textbf{Video Categories}} & \multirow{2}{*}{\textbf{Overall}} \\
+        & Live-action & Animation  & Stock & YouTube & Shorts & \\\midrule
+        \multicolumn{7}{l}{\textit{Proprietary models}} \\
+        GPT-4V \cite{gpt4v} & 34.8/39.2/31.3 & 27.4/31.9/24.0 & 40.7/\underline{46.7}/36.1 & 33.8/40.1/29.2 & 34.8/46.1/28.0 & 34.4/40.8/29.7 \\
+        GPT-4o \cite{gpt4o} & 39.8/\underline{42.1}/37.8 & 35.8/39.1/33.1 & 44.0/46.6/41.7 & 35.9/\underline{41.5}/31.7 & 39.9/47.9/34.2 & 39.2/\underline{43.4}/35.7 \\
+        Gemini-1.5-Flash \cite{geminiteam2024gemini15unlockingmultimodal} & 34.8/36.4/33.3 & 29.2/32.5/26.5 & 39.4/39.7/39.1 & 34.3/38.6/30.9 & 35.6/42.4/30.7 & 34.8/37.9/32.1 \\
+        Gemini-1.5-Pro \cite{geminiteam2024gemini15unlockingmultimodal} & 36.4/36.4/36.4 & 30.7/31.8/29.7 & 42.2/40.7/43.8 & 34.0/36.7/31.6 & 37.0/42.4/32.7 & 36.2/37.6/34.8 \\
+        \midrule
+        \multicolumn{7}{l}{\textit{Open-source models ($>$10B)}} \\
+        PLLaVA-34B \cite{xu2024pllava} & 29.3/34.9/25.2 & 20.9/32.0/15.6 & 35.1/42.5/29.9 & 28.9/40.8/22.3 & 25.6/41.9/18.4 & 28.2/38.4/22.3 \\
+        VideoLLaMA2-72B \cite{cheng2024videollama2} & 27.3/29.3/25.6 & 19.7/21.7/18.1 & 33.9/37.0/31.3 & 27.7/33.0/23.8 & 26.5/33.1/22.1 & 27.1/30.8/24.2 \\
+        LLaVA-OV-72B \cite{li2024llavanext} & 31.7/32.8/30.7 & 27.7/30.6/25.2 & 38.0/39.6/36.6 & 34.1/34.7/33.5 & 33.8/41.8/28.4 & 33.2/35.9/30.9 \\
+        LLaVA-Video-72B \cite{zhang2024video} & 33.5/36.3/31.1 & 28.6/31.7/26.1 & 39.3/41.1/37.6 & 32.8/34.7/31.1 & 35.7/42.8/30.6 & 34.0/37.3/31.3\\
+        Qwen2-VL-72B \cite{qwen2vl} & 32.1/33.7/30.6 & 27.6/32.6/23.9 & 41.1/41.2/41.1 & 32.0/38.1/27.7 & 32.1/41.0/26.4 & 33.2/37.3/29.9\\
+        InternVL2.5-78B \cite{chen2024expanding} & 25.3/31.5/21.1 & 21.8/28.8/17.6 & 33.5/38.1/29.9 & 31.0/38.5/25.9 & 31.1/41.7/24.8 & 28.6/35.7/23.9\\
+        Tarsier-34B \cite{wang2024tarsierrecipestrainingevaluating} & 38.5/39.6/37.5 & 32.2/35.8/29.2 & 41.7/46.4/37.8 & 34.5/41.1/29.7 & 34.0/44.1/27.7 & 36.3/41.4/32.4  \\
+        % VILA-40B \\
+        \midrule
+        \multicolumn{7}{l}{\textit{Open-source models ($<$10B)}} \\
+        Video-LLaVA-7B \cite{lin2023video} & 19.4/24.3/16.2 & 15.3/21.2/11.9 & 27.0/33.5/22.7 & 21.2/31.9/15.8 & 18.5/29.4/13.5 & 20.4/28.1/16.0 \\
+        VideoLLaMA2-7B \cite{cheng2024videollama2} & 25.1/28.7/22.2 & 20.4/25.5/17.0 & 32.6/35.5/30.2 & 27.5/33.5/23.4 & 24.5/34.1/19.2 & 26.2/31.5/22.4 \\
+        LLaVA-OV-7B \cite{li2024llavanext} & 31.2/33.2/29.3 & 26.8/29.0/25.0 & 38.1/39.1/37.1 & 30.6/32.1/29.2 & 31.4/38.3/26.6 & 31.7/34.3/29.4 \\
+        LLaVA-Video-7B \cite{zhang2024video} & 31.4/35.2/28.4 & 27.6/32.9/23.8 & 36.7/39.7/34.1 & 33.0/\textbf{39.5}/28.3 & 33.4/42.5/27.5 & 32.5/37.9/28.4 \\
+        Qwen2-VL-7B \cite{qwen2vl} & 27.7/32.5/24.2 & 22.2/28.0/18.4 & 37.0/36.1/38.0 & 30.7/35.5/27.0 & 29.1/37.6/23.8 & 29.6/33.9/26.3 \\ 
+        InternVL2.5-8B \cite{chen2024expanding} & 26.6/32.0/22.8 & 21.3/28.9/16.9 & 32.7/37.2/29.1 & 27.9/35.4/23.0 & 28.9/39.9/22.7 & 27.6/34.7/22.9 \\
+        Tarsier-7B \cite{wang2024tarsierrecipestrainingevaluating} & 36.6/38.5/34.8 & 29.3/34.6/25.5 & 39.6/44.7/35.5 & 33.0/39.2/28.4 & 33.6/44.6/26.9 & 34.6/40.3/30.2 \\
+        \midrule
+        Tarsier2-7B & \underline{\textbf{44.4}}/\textbf{41.9}/\underline{\textbf{47.3}} & \underline{\textbf{39.3}}/\underline{\textbf{39.5}}/\underline{\textbf{39.1}} & \underline{\textbf{45.7}}/\textbf{45.4}/\underline{\textbf{46.0}} & \underline{\textbf{36.0}}/38.4/\underline{\textbf{33.9}} & \underline{\textbf{43.7}}/\underline{\textbf{48.9}}/\underline{\textbf{39.4}} & \underline{\textbf{42.0}}/\textbf{42.8}/\underline{\textbf{41.1}} \\
+        \bottomrule
+        \end{tabular}
+    }
+    \caption{Evaluation results on DREAM-1K. We report F1/Precision/Recall scores for each category and for the overall dataset. For open-source models, all results are tested with their official checkpoint and inference code under recommended setting. SOTA results of comparable scale ($<$10B) are bolded and overall best results are underlined.}
+    \label{tab:dream-1k}
+    \end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/10.tex

@@ -0,0 +1,19 @@
+\begin{table}[t]
+    \centering
+    \scriptsize
+    \setlength{\tabcolsep}{3pt} % 调整列间距
+    \resizebox{\textwidth}{!}{\begin{tabular}{l|ccc|cc|c}
+    \toprule
+    \multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Caption}} & \multicolumn{2}{c|}{\textbf{Video QA}} & \multirow{2}{*}{\textbf{Hallucination}} \\
+    & DREAM-1K & TempCompass-cg & Vinoground-Text & Short & Long & \\
+    \midrule
+    Tarsier2-7B & 42.0 & 66.6 & 65.8 & 56.1 & 62.8 & 74.0  \\
+    \midrule
+    \quad \textit{w/o DPO} & 40.8 ($\downarrow$1.2) & 62.1 ($\downarrow$6.5) & 60.6 ($\downarrow$5.6) & 56.2 ($\uparrow$0.1) & 63.2 ($\uparrow$0.4) & 71.9 ($\downarrow$2.1) \\
+    \quad \textit{w/o NS} & 41.5 ($\downarrow$0.5) & 61.1 ($\downarrow$5.5) & 59.8 ($\downarrow$6.0)& 56.1 ($\downarrow$0.0)  & 62.8 ($\downarrow$0.0) & 72.9 ($\downarrow$1.1) \\
+    \quad \textit{w/o PF} & 40.5 ($\downarrow$1.5) & 65.1 ($\downarrow$1.5) & 67.6 ($\uparrow$1.8) & 56.0 ($\downarrow$0.1) & 62.3 ($\downarrow$0.5) & 74.2 ($\uparrow$0.2) \\
+    \bottomrule
+    \end{tabular}}
+    \caption{Ablation study for DPO training phase, negative sampling (NS) and preference data filtering (PF) strategies.}
+    \label{tab:dpo_ablation}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/11.tex

@@ -0,0 +1,24 @@
+\begin{table}[t]
+    \centering
+    \scriptsize
+    \setlength{\tabcolsep}{3pt} % 调整列间距
+    \resizebox{\textwidth}{!}{\begin{tabular}{l|ccc|cc|c}
+    \toprule
+    \multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Caption}} & \multicolumn{2}{c|}{\textbf{Video QA}} & \multirow{2}{*}{\textbf{Hallucination}} \\
+    & DREAM-1K & TempCompass-cg & Vinoground-Text & Short & Long & \\
+    \midrule
+    % Tarsier2-7B & 42.0 & 66.6 & 65.8 & 56.1 & 62.6 & 74.0  \\
+    % \midrule
+    % Tarsier-7B & 34.6 & 55.3 & 29.8 & 45.6 & 46.3 & 56.3 \\
+    % \makecell[l]{+ \textit{Recaption FT}} & 31.6 & 52.9 & 27.4 & 44.1 & 45.2 & 46.3 \\
+    % \makecell[l]{+ \textit{Original FT}} & xxx & xxx & xxx & xxx & xxx & xxx \\
+    % \midrule
+    Qwen2-VL-7B \cite{qwen2vl} & 31.2 & 54.2 & 40.0 & 49.4 & 60.3 & 51.9 \\
+    \midrule
+    \makecell[l]{+ \textit{Original FT}} & 35.2 ($\uparrow$4.0) & 49.9 ($\downarrow$4.3) & 39.0 ($\downarrow$1.0) & 46.9 ($\downarrow$2.5) & 55.4 ($\downarrow$4.9) & 43.0 ($\downarrow$8.9) \\
+    \makecell[l]{+ \textit{Recaption FT}} & 39.5  ($\uparrow$8.3) & 67.7 ($\uparrow$13.5) & 55.0 ($\uparrow$15.0) & 52.5 ($\uparrow$3.1) & 56.8 ($\downarrow$3.5) & 68.5 ($\uparrow$16.6) \\
+    \bottomrule
+    \end{tabular}}
+    \caption{The experimental results of recaptioning. ``\textit{Recaption FT}'' represents fine-tune the model on the Tarsier2-Recap-585K dataset. ``\textit{Original FT}'' represents fine-tune the model with the same videos as Tarsier2-Recap-585K but taking their original labels as target output.}
+    \label{tab:recaption}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/12.tex

@@ -0,0 +1,28 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{\textwidth}{!}{
+    \begin{tabular}{l |c c c c}
+         \toprule
+         \textbf{Configuration}   & \textbf{Pre-training} & \textbf{SFT-1} & \textbf{SFT-2} & \textbf{DPO} \\\midrule
+         VLM init.                & Qwen2-VL-7B & Tarsier2-Pre-trian & Tarsier2-SFT-1 & Tarsier2-SFT-2 \\
+         Optimizer name           & \multicolumn{4}{c}{AdamW} \\
+         Optimizer $\beta_1$      & \multicolumn{4}{c}{$0.9$}\\
+         Optimizer $\beta_2$      & \multicolumn{4}{c}{$0.999$}\\
+         Optimizer eps            & \multicolumn{4}{c}{$1e^{-6}$}\\
+         Learning rate            & $2e^{-5}$ & $2e^{-5}$ & $2e^{-6}$ & $1e^{-6}$\\
+         Learning rate schedule   & \multicolumn{4}{c}{cosine} \\
+         Training steps           & 200,000 & 5,000 & 5,000 & 1,000\\
+         Warm-up steps            & 1,000 & 250 & 250 & 100 \\
+         Weight decay             & \multicolumn{4}{c}{0.01}\\
+         Gradient clip            & \multicolumn{4}{c}{1.0} \\
+         Dropout rate             & \multicolumn{4}{c}{0.0}\\
+         Global batch size        & 384 & 64 & 64 & 64 \\
+         Max pixels         & \multicolumn{4}{c}{460,800} \\
+         Frames per video         & [8,128] & 16 & 16 & 16 \\
+         Numerical precision      & \multicolumn{4}{c}{bfloat16} \\
+         \bottomrule
+    \end{tabular}
+    }
+    \caption{Training hyper-parameters of \modelname}
+    \label{tab:hyperparam}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/13.tex

@@ -0,0 +1,109 @@
+\begin{table}[h!]
+    \centering
+    \small
+    \setlength{\tabcolsep}{3pt} % 调整列间距
+    \resizebox{\textwidth}{!}{
+    \begin{tabular}{llll}
+    \toprule
+    \multicolumn{4}{l}{\textit{\textbf{Video Captioning}}} \\
+    WebVid~\cite{bain2021frozen} (2.9M) & 
+    LSMDC~\cite{rohrbach2017movie} (109K) &
+    TGIF~\cite{li2016tgif} (105K) & 
+    ActivityNet~\cite{krishna2017dense} (38K) \\
+    Charades~\cite{sigurdsson2016hollywood} (16K) & 
+    Charades-Ego~\cite{sigurdsson2018charades} (6K) & 
+    YouCook2~\cite{zhou2018youcook2} (9K) &
+    TACoS~\cite{regneri2013grounding} (18K)\\
+    Ego4D~\cite{grauman2022ego4d} (1.1M) &
+    Spoken Moments~\cite{monfort2021spoken} (493K) & 
+    Multi-Moments~\cite{monfort2021multi} (997K) &
+    TREC-VTT~\cite{awad2023trecvid} (64K) \\
+    ShareGPT-4o-video~\cite{sharegpt4o} (2K) &
+    MovieStory101\cite{he2024storyteller} (11K)  &
+    GPT4o-labeled Caption$^\dagger$ (2.5M) &
+    Human-labeled Caption$^\dagger$ (145K) \\
+    Film\&TV Commentary$^\dagger$ (11.5M) & 
+    \\
+    
+    \midrule
+    \multicolumn{4}{l}{\textit{\textbf{Action Recognition}}} \\
+    HMDB~\cite{kuehne2011hmdb} (5.8K) & 
+    COIN~\cite{tang2019coin} (10K) & 
+    SSV2~\cite{goyal2017something} (169K) &
+    Kinetics-700~\cite{carreira2017quo} (537K) \\
+    FineAction~\cite{liu2022fineaction} (82K) & 
+    RareAct~\cite{miech2020rareact} (2K) & 
+    20BN-jester~\cite{materzynska2019jester} (46K) & \\
+    
+    \midrule
+    \multicolumn{4}{l}{\textit{\textbf{Video QA}}} \\
+    CLEVRER~\cite{yi2019clevrer} (83K) & 
+    TGIF-QA~\cite{jang2017tgif} (72K) & 
+    EgoQA~\cite{fan2019egovqa} (5K) &
+    VideoInstruct~\cite{maaz2023video} (89K) \\
+    LLaVA-Video-178K~\cite{zhang2024video} (165K) & 
+    M4-Instruct-video~\cite{li2024llava} (255K) & 
+    GPT4o-labeled QA$^\dagger$ (16.2K) & 
+    \\
+    
+    \midrule
+    \multicolumn{4}{l}{\textit{\textbf{Grounding}}} \\
+    DiDeMo~\cite{anne2017localizing} (82K) & 
+    AVA~\cite{gu2018ava} (28K) & 
+    E.T. Instruct 164K~\cite{liu2024etbench} (147K) & 
+    Object Tracking$^\dagger$ (745K) \\
+    
+    \midrule
+    \multicolumn{4}{l}{\textit{\textbf{Video Self-Supervised Training}}} \\
+    Frame Order Prediction$^\dagger$ (825K) \\
+    
+    \midrule
+    \multicolumn{4}{l}{\textit{\textbf{Intent Recognition}}} \\
+    Oops!~\cite{epstein2020oops} (15K) & & & \\
+    
+    
+    
+    \midrule
+    \multicolumn{4}{l}{\textit{\textbf{Multi-Image Understanding}}} \\
+    VIST~\cite{huang2016visual} (38K) & 
+    MMDU~\cite{liu2024mmdu} (45K) & 
+    M4-Instruct-image~\cite{li2024llava} (616K) & 
+    Image Retrival$^\dagger$ (533K) \\
+    
+    \midrule
+    \multicolumn{4}{l}{\textit{\textbf{Single-Image Understanding}}} \\
+    ShareGPT4V~\cite{chen2023sharegpt4v} (95K) &
+    LLaVA-1.5~\cite{liu2023improved} (643K) &
+    ShareGPT-4o-image\cite{sharegpt4o} (57K) & 
+    MS COCO~\cite{lin2014microsoft} (566K) \\
+    Flicker~\cite{plummer2015flickr30k} (145K) & 
+    LLaVA-ReCap-CC3M~\cite{li2024llava} (2.9M) & 
+    Visual Genome~\cite{krishna2017visual} (759K) & 
+    SBU Captions~\cite{ordonez2011im2text} (860K) \\
+    GPT4o-labeled Caption$^\dagger$ (1.13M) \\
+    
+    \midrule
+    \multicolumn{4}{l}{\textit{\textbf{Image OCR}}} \\
+    RCTW-17~\cite{shi2017icdar2017} (8K) & 
+    LSVT~\cite{sun2019icdar} (430K) & 
+    ReCTS~\cite{zhang2019icdar} (20K) & 
+    Art~\cite{bhagavatula2019abductive} (5.6K) \\
+    COCOTextV2~\cite{veit2016coco} (16K) & 
+    CORD-v2~\cite{park2019cord} (1K) & 
+    HierText~\cite{long2022towards} (10K) & 
+    MSRA-TD500~\cite{yao2012detecting} (465) \\
+    IC03~\cite{lucas2005icdar} (499) & 
+    SynthDoG-en~\cite{kim2022donut} (100K) & 
+    SynthDoG-zh~\cite{kim2022donut} (100K) & \\
+    
+    \midrule
+    \multicolumn{4}{l}{\textit{\textbf{Text Generation}}} \\
+    OpenOrca~\cite{lian2023openorca} (995K) &
+    ShareGPT~\cite{vicuna2023} (80K) & & \\
+    
+    \bottomrule
+    \end{tabular}
+    }
+    \caption{Datasets and their sizes used in \modelname pre-training. $\dagger$ indicates in-house datasets.}
+    \label{tab:pretraining-datasets}
+    \end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/14.tex

@@ -0,0 +1,28 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{0.9\textwidth}{!}{%
+    \begin{tabular}{cl|ccc}
+    \toprule
+        \textbf{Capability} & \textbf{Benchmark} & \textbf{Tarsier1-7B} & \textbf{Tarsier1-7B-Qwen} & \textbf{Tarsier2-7B} \\
+        
+    \midrule
+    \multirow{3}{*}{Caption} & DREAM-1K & 34.6/30.2/40.3 & 38.4/40.6/36.4 & 40.8/42.5/39.3 \\
+    & TempCompass-cg & 55.3 & 59.3 & 60.1 \\
+    & Vinoground-Text & 29.8 & 48.6 & 60.2 \\
+    \midrule
+    \multirow{3}{*}{Video QA Short} & MVBench & 62.6 & 69.8 & 72.8  \\
+    & TVBench & 45.8 & 51.0 & 53.5 \\
+    & TOMATO & 28.6 & 36.5 & 39.5 \\
+    \midrule
+    \multirow{3}{*}{Video QA Long} & Video-MME & 42.2 & 58.9 & 65.3 \\
+    & LongVideoBench & 39.8 & 52.1 & 58.3 \\
+    & TemporalBench & 56.9 & 61.9 & 68.7 \\
+    \midrule
+    \multirow{2}{*}{Hallucination} & EventHallusion-Y/N & 70.9 & 75.6 & 77.8 \\
+    & EventHallusion-Desc & 41.6 & 48.6 & 49.1\\
+    \bottomrule
+    \end{tabular}
+    }
+    \caption{Detailed results of the ablation study for pre-training. For the captioning task, results are reported after the SFT stage. For other tasks, results are reported after the pre-training stage. }
+    \label{tab:appendix-pretrain_detailed_results}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/15.tex

@@ -0,0 +1,27 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{0.9\textwidth}{!}{%
+    \begin{tabular}{cl|ccc}
+    \toprule
+        \textbf{Capability} & \textbf{Benchmark} & \makecell[c]{\\ pre-train} & \makecell[c]{\textbf{Tarsier2-7B}\\ SFT w/o grounding} & \makecell[c]{\\ SFT} \\
+    \midrule
+    \multirow{3}{*}{Caption} & DREAM-1K & 35.2/36.8/33.7 & 37.4/38.6/36.3  & 40.8/42.5/39.3 \\
+    & TempCompass-cg & 50.5 & 50.2 & 60.1 \\
+    & Vinoground-Text & 57.2 & 60.6 & 60.2 \\
+    \midrule
+    \multirow{3}{*}{Video QA Short} & MVBench & 72.8 & 71.9 & 72.5  \\
+    & TVBench & 53.5 & 54.5 & 54.2 \\
+    & TOMATO & 39.5 & 41.3 & 41.9 \\
+    \midrule
+    \multirow{3}{*}{Video QA Long} & Video-MME & 65.3 & 64.0 & 64.7 \\
+    & LongVideoBench & 58.3 & 54.7 & 58.2 \\
+    & TemporalBench & 68.7 & 66.9 & 66.6 \\
+    \midrule
+    \multirow{2}{*}{Hallucination} & EventHallusion-Y/N & 77.8 & 80.1 & 84.4 \\
+    & EventHallusion-Desc & 49.1 & 56.2 & 59.4 \\
+    \bottomrule
+    \end{tabular}
+    }
+    \caption{Detailed results of the ablation study for SFT.}
+    \label{tab:appendix-sft_detailed_results}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/16.tex

@@ -0,0 +1,27 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{0.9\textwidth}{!}{%
+    \begin{tabular}{cl|cccc}
+    \toprule
+        \textbf{Capability} & \textbf{Benchmark} & \textbf{Tarsier2-7B} & \textit{w/o DPO} & \textit{w/o NS} & \textit{w/o PF}\\
+    \midrule
+    \multirow{3}{*}{Caption} & DREAM-1K & 42.0/42.8/41.1 & 40.8/42.5/39.3 & 41.5/44.5/39.0 & 40.5/39.9/41.1 \\
+    & TempCompass-cg & 66.6 & 60.1 & 62.1 & 65.1 \\
+    & Vinoground-Text & 65.8 & 60.2 & 60.6 & 67.6 \\
+    \midrule
+    \multirow{3}{*}{Video QA Short} & MVBench & 71.5 & 72.5 & 72.2 & 71.7 \\
+    & TVBench & 54.7 & 54.2 & 54.9 & 54.6 \\
+    & TOMATO & 42.0 & 41.9 & 41.3 & 41.8 \\
+    \midrule
+    \multirow{3}{*}{Video QA Long} & Video-MME & 64.5 & 64.7 & 64.3 & 64.4 \\
+    & LongVideoBench & 58.6 & 58.2 & 58.6 & 57.4 \\
+    & TemporalBench  & 65.3 & 66.6 & 65.4 & 65.2 \\
+    \midrule
+    \multirow{2}{*}{Hallucination} & EventHallusion-Y/N & 84.6 & 84.4 & 85.1 & 84.8 \\
+    & EventHallusion-Desc & 63.3 & 59.4 & 60.7 & 63.5 \\
+    \bottomrule
+    \end{tabular}
+    }
+    \caption{Detailed results of the ablation study for DPO.}
+    \label{tab:appendix-dpo_detailed_results}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/17.tex

@@ -0,0 +1,27 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{0.9\textwidth}{!}{%
+    \begin{tabular}{cl|ccc}
+    \toprule
+        \textbf{Capability} & \textbf{Benchmark} & \textbf{Qwen2-VL-7B} \cite{qwen2vl} & \textit{$+$ Original FT} & \textit{$+$ Recaption FT} \\
+    \midrule
+    \multirow{3}{*}{Caption} & DREAM-1K & 29.6/33.9/26.3 & 35.2/44.8/29.0 & 39.5/41.7/37.6 \\
+    & TempCompass-cg & 54.2 & 49.9 & 67.7 \\
+    & Vinoground-Text & 40.0 & 39.0 & 55.0 \\
+    \midrule
+    \multirow{3}{*}{Video QA Short} & MVBench & 67.0 & 59.8 & 66.8 \\
+    & TVBench & 43.8 & 47.2 & 51.1 \\
+    & TOMATO & 31.5 & 33.6 & 39.5 \\
+    \midrule
+    \multirow{3}{*}{Video QA Long} & Video-MME & 63.3 & 56.1 & 57.0\\
+    & LongVideoBench & 55.6 & 51.4 & 51.9 \\
+    & TemporalBench  & 62.0 & 58.7 & 61.4 \\
+    \midrule
+    \multirow{2}{*}{Hallucination} & EventHallusion-Y/N & 68.6 & 39.6 & 80.7 \\
+    & EventHallusion-Desc & 27.8 & 46.3 & 56.2 \\
+    \bottomrule
+    \end{tabular}
+    }
+    \caption{Detailed results of the recaptioning experiment.}
+    \label{tab:appendix-recap_detailed_results}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/18.tex

@@ -0,0 +1,32 @@
+\begin{table}[t]
+    \centering
+    \resizebox{\textwidth}{!}{%
+    \begin{tabular}{lccrrr}
+    \toprule
+        \textbf{Dataset} & \textbf{Original Label Type} & \textbf{Split} & \textbf{Avg Duration (s)} & \textbf{\# Sampled Clips} & \textbf{Proportion (\%)} \\
+    \midrule
+    % \midrule
+    WebVid-10M~\cite{bain2021frozen} & \multirow{9}{*}{Video Caption} & - & 15.2 & 177,909 & 30.38 \\
+    LSMDC~\cite{rohrbach2017movie} &  & \textbf{train}/\textbf{val}/\textbf{test} & 4.1 & 108,271 & 18.49 \\
+    TGIF~\cite{li2016tgif} &  & \textbf{train}/test & 12.3 & 94,775 & 16.18 \\
+    Ego4D~\cite{grauman2022ego4d} &  & - & 4.1 & 50,000 & 8.54 \\
+    ActivityNet~\cite{krishna2017dense} &  & \textbf{train}/\textbf{val}/test & 35.7 & 35,960 & 6.14 \\
+    VATEX~\cite{wang2019vatex} &  & \textbf{train}/\textbf{val}/test & 10.0 & 22,435 & 3.83 \\
+    TREC-VTT~\cite{awad2023trecvid} & & \textbf{train}/val & 6.3 & 14,199 & 2.42 \\
+    Charades~\cite{sigurdsson2016hollywood} & & \textbf{train}/test & 29.8 & 7,985 & 1.36 \\
+    Charades-Ego~\cite{sigurdsson2018charades} & & \textbf{train}/test & 30.2 & 6,161 & 1.05 \\
+    \midrule
+    % \midrule
+    Kinetics-700~\cite{carreira2017quo} & \multirow{2}{*}{Action Recognition} & \textbf{train}/val/test & 8.9 & 50000 & 8.50 \\
+    SSV2~\cite{goyal2017something} & & \textbf{train}/val/test & 3.7 & 10000 & 1.71 \\
+    \midrule
+    % \midrule
+    Oops~\cite{epstein2020oops} & Intent Recognition & \textbf{train}/\textbf{val} & 9.8 & 7,948 & 1.36 \\
+    \midrule
+    \textbf{Sum} & - & - & \textbf{1,972 hours} & \textbf{585,643} & \textbf{100.00} \\
+    \bottomrule
+    \end{tabular}
+    }
+    \caption{Data composition of Tarsier2-Recap-585K. The ``Split'' column lists the original dataset partitioning, and we use bold to mark the parts which we sampled the video clips from to conduct recaptioning.}
+    \label{tab:recaption_composition}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/2.tex

@@ -0,0 +1,39 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{0.8\textwidth}{!}{%
+    \begin{tabular}{l|cccccc}
+    \toprule
+        \multirow{2}{*}{\textbf{Model}} & \multicolumn{6}{c}{\textbf{E.T. Bench-Captioning} \cite{liu2024etbench}} \\
+        & DVC$_{F1}$ & DVC$_{Sim}$ & SLC$_{F1}$ & SLC$_{Sim}$ & \textbf{Avg}$_{F1}$ & \textbf{Avg}$_{Sim}$\\
+    \midrule
+    \multicolumn{5}{l}{\textit{Proprietary models}} \\
+        GPT-4V \cite{gpt4v} & \color{lightgray}16.1 & \color{lightgray}19.4 & \color{lightgray}21.9 & \color{lightgray}13.5 & \color{lightgray}19.0 & \color{lightgray} 16.4\\
+        GPT-4o \cite{gpt4o} & \color{lightgray}\underline{46.9} & \color{lightgray}22.3 & \color{lightgray}23.1 & \color{lightgray}14.9 & \color{lightgray}35.0 & \color{lightgray}18.6\\
+        Gemini-1.5-Flash \cite{geminiteam2024gemini15unlockingmultimodal} & \color{lightgray}31.6 & \color{lightgray}14.9 & \color{lightgray}16.5 & \color{lightgray}13.3 & \color{lightgray}24.1 & \color{lightgray}14.1\\
+        Gemini-1.5-Pro \cite{geminiteam2024gemini15unlockingmultimodal} & \color{lightgray}24.0 & \color{lightgray}17.5 & \color{lightgray}5.8 & \color{lightgray}9.8 & \color{lightgray}14.9 & \color{lightgray}13.7\\
+    \midrule
+    \multicolumn{5}{l}{\textit{Open-source models ($>$10B)}} \\
+        PLLaVA-34B \cite{xu2024pllava} &13.3 & 10.6 & 9.7 & 11.8 & 11.5 & 11.2 \\
+        % VILA-40B & 33.2 & 37.6 & 29.7& - & - & -& -\\
+        LLaVA-OV-72B \cite{li2024llavanext} & 41.9 & 16.3 & 25.6 & 13.9 & 33.8 & 15.1 \\
+        LLaVA-Video-72B \cite{zhang2024video} & 37.0 & 15.7 & 20.4 & 13.5 & 28.7 & 14.6 \\   
+        Qwen2-VL-72B \cite{qwen2vl} & 15.3 & 13.9 & 11.0& 12.8 & 13.2 & 13.4 \\
+    \midrule
+    \multicolumn{5}{l}{\textit{Open-source models ($\leq$10B)}} \\
+       VideoLLaMA2-7B \cite{cheng2024videollama2} & 0.6 & 14.5 & 0.0 & 15.2 & 0.3 & 14.8 \\
+       Video-LLaVA-7B \cite{lin2023video} & 28.0 & 15.0 & 0.9 & 8.3 & 14.4 & 11.7\\
+       LLaVA-OV-7B \cite{li2024llavanext} & 22.0 & 15.1 & 9.5 & 10.6 & 15.8 & 12.8 \\
+       LLaVA-Video-7B \cite{zhang2024video} & 20.6 & 14.7 & 6.5 & 13.4 & 13.6 & 14.1 \\
+       E.T. Chat \cite{liu2024etbench} $^\dag$ & 38.4 & 19.7 & 24.4 & 14.6 & 31.4 & 17.1 \\
+       % Qwen2-VL-7B & 12.9 & 13.3 & 4.5 & 11.7 \\
+       Qwen2-VL-7B \cite{qwen2vl} $^\dag$ & 44.3 & 25.3 & \underline{\textbf{25.7}} & 15.6 & 35.0 & 20.4 \\      
+       Tarsier-7B \cite{wang2024tarsierrecipestrainingevaluating} $^\dag$ & 42.8 & 19.1 & 23.7 & 15.2 & 33.2 & 17.1 \\
+    \midrule
+        % Tarsier2-7B & 30.3 & 18.2 & 16.6 & 11.8 \\
+        Tarsier2-7B $^\dag$& \textbf{46.5} & \underline{\textbf{28.8}} & 24.6 & \underline{\textbf{16.4}} & \underline{\textbf{35.5}} & \underline{\textbf{22.6}} \\
+    \bottomrule
+    \end{tabular}%
+    }
+    \caption{Evaluation results on E.T. Bench-Captioning. Results marked in gray are tested on a subset. $\dag$ denotes the model is fine-tuned on E.T. Instruct 164K. All results are transcribed from the official benchmark, except for LLaVA-OV, LLaVA-Video and Qwen2-VL, which are our evaluation using the official checkpoint and inference code.}
+    \label{tab:dense_caption}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/3.tex

@@ -0,0 +1,43 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{0.98\textwidth}{!}{%
+    \begin{tabular}{l|cccccc}
+    \toprule
+        \multirow{2}{*}{\textbf{Model}} & \textbf{MVBench}\cite{li2024mvbench} & \textbf{PerceptionTest}\cite{patraucean2024perception} & \textbf{TVBench}\cite{cores2024tvbench} & \textbf{TOMATO}\cite{shangguan2024tomato} & \textbf{Vinoground}\cite{zhang2024vinoground} & \textbf{TempCompass}\cite{liu2024tempcompass} \\
+        & test & val & test & test & Text/Video/Group & mc/yn/cm/cg\\
+    \midrule
+    \multicolumn{6}{l}{\textit{Proprietary models}} \\
+        % GPT-4V & 43.5& -& - & - & - \\
+        GPT-4o \cite{gpt4o} & 57.5 & -&39.6 & 37.7 & 54.0/\underline{38.2}/24.6 & 71.0/73.7/80.8/70.8 \\
+        % Gemini-1.5-Flash\cite{geminiteam2024gemini15unlockingmultimodal} & 54.1& -& - & 27.8 & - \\
+        Gemini-1.5-Pro \cite{geminiteam2024gemini15unlockingmultimodal} & -& -& 46.5 & 36.1 & 35.8/22.6/10.2 & 63.9/70.3/77.5/57.9\\
+    \midrule
+    \multicolumn{6}{l}{\textit{Open-source models ($>$10B)}} \\
+        % VILA-40B & 43.2& 54.0& 45.4 & - & 41.2/23.2/8.8 \\
+        % PLLaVA-34B & 58.1&- & 41.9 & - & - \\
+        % VideoLLaMA2-72B & 62.0& -& - & - & 36.2/21.8/8.4\\
+        LLaVA-OV-72B \cite{li2024llavanext} & 59.4& 66.9& 45.9 & 28.6 & 48.4/35.2/21.8 & 67.6/72.6/78.2/52.6 \\
+        LLaVA-Video-72B \cite{zhang2024video} & 64.1& 
+\underline{74.3}*& 50.0 & 28.2 & 52.0/35.6/20.8 & 69.9/73.0/80.9/54.4 \\
+        Qwen2-VL-72B \cite{qwen2vl} & 
+\underline{73.6} & 66.5 & 52.7 & 37.9 & 50.4/32.6/17.4 & \underline{76.0}/\underline{75.9}/\underline{84.6}/58.6 \\
+        Tarsier-34B \cite{wang2024tarsierrecipestrainingevaluating} & 67.6&60.4&53.8&34.3&37.8/32.0/15.0 & 69.8/74.0/73.0/60.9 \\
+        % \textbf{SOTA} & \\
+    \midrule
+    \multicolumn{6}{l}{\textit{Open-source models ($\leq$10B)}} \\
+        % LongVA-7B & -& -& - & - & - \\
+        % IXC-2.5-7B & 69.1& 34.4& - & - & - \\
+        LLaVA-OV-7B \cite{li2024llavanext} & 56.7& 57.1& 45.6 & 25.5& 41.6/29.4/14.6 & 64.8/69.7/73.8/49.9\\
+        LLaVA-Video-7B \cite{zhang2024video} & 58.6 & 67.9*& 45.6 & 24.9 & 36.8/29.0/12.8 & 56.3/68.7/76.8/53.0\\
+        Qwen2-VL-7B \cite{qwen2vl} & 67.0& - & 43.8 & 31.5& 40.0/23.4/12.4 & 68.5/72.8/77.3/54.2\\
+        Tarsier-7B \cite{wang2024tarsierrecipestrainingevaluating} & 62.6&53.9&45.8&28.6&29.8/22.2/8.6 & 58.7/58.0/54.2/55.3\\
+        Previous SOTA & \textbf{72.0} \cite{chen2024expanding} & 70.0* \cite{liu2024oryx} & 51.6 \cite{zhang2024internlmxcomposer} & 31.5 \cite{qwen2vl} & 41.6/29.4/14.6 \cite{li2024llava} &
+        68.5/72.8/77.3/54.2 \cite{qwen2vl}\\
+    \midrule
+        Tarsier2-7B & 71.5& \textbf{71.6}* & \underline{\textbf{54.7}} & \underline{\textbf{42.0}} & \underline{\textbf{65.8}}/\textbf{38.0}/\underline{\textbf{28.8}} & \textbf{75.3}/\textbf{75.1}/\textbf{80.6}/\underline{\textbf{66.6}}\\
+    \bottomrule
+    \end{tabular}%
+    }
+    \caption{Evaluation results on short video question answering benchmarks. * indicates that the training set has been observed in the training data mixture.}
+    \label{tab:my_label}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/4.tex

@@ -0,0 +1,35 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{0.98\textwidth}{!}{%
+    \begin{tabular}{l|ccccc}
+    \toprule
+        \multirow{2}{*}{\textbf{Model}} & \textbf{Video-MME}\cite{fu2024video} & \textbf{LongVideoBench}\cite{wu2024longvideobench} & \textbf{TemporalBench}\cite{cai2024temporalbench} & \textbf{MLVU}\cite{zhou2024mlvu} &\textbf{MMBench-Video}\cite{fang2024mmbench}\\
+         & w/o subs & val & Binary Accuracy & M-Avg & val \\
+    \midrule
+    \multicolumn{6}{l}{\textit{Proprietary models}} \\
+    GPT-4o \cite{gpt4o} &  71.9 & \underline{66.7} & \underline{73.2} & 64.6 & 1.87 \\
+    Gemini-1.5-Pro \cite{geminiteam2024gemini15unlockingmultimodal} & \underline{75.0} & 64.0 & 66.4 & - & 1.30 \\
+    
+    \midrule
+    \multicolumn{6}{l}{\textit{Open-source models ($>$10B)}} \\
+    VILA-1.5-40B \cite{lin2024vila} & 60.1 & - & - & 56.7 & 1.61 \\
+    LLaVA-Video-72B \cite{zhang2024video} & 70.5 & 61.9 & 72.4 & 74.4 & 1.71 \\
+    Qwen2-VL-72B \cite{qwen2vl} & 71.2 & - & 70.2 & - & 1.70 \\
+    InternVL2.5-78B \cite{chen2024expanding} & 72.1 & 63.6 & - & \underline{75.7} & 
+\underline{1.97} \\
+    Tarsier-34B \cite{wang2024tarsierrecipestrainingevaluating} & 52.3 & 54.2 & 66.7 & 58.2 & 1.46 \\
+    \midrule
+    \multicolumn{6}{l}{\textit{Open-source models ($\leq$10B)}} \\
+    LLaVA-Video-7B \cite{zhang2024video} & 63.3 & 58.2 & 63.6 & 70.8 & 1.60 \\
+    Qwen2-VL-7B \cite{qwen2vl} & 63.3 & 55.6 & 62.0 & - & 1.44 \\
+    InternVL2.5-8B \cite{chen2024expanding} & 64.2 & 60.0 & - & 68.9 & 1.68 \\ 
+    Tarsier-7B \cite{wang2024tarsierrecipestrainingevaluating} & 42.2 & 39.8 & 56.9 & 49.3 & - \\
+    Previous SOTA & 64.2 \cite{liu2024nvila} & 60.0 \cite{chen2024expanding} & 63.6 \cite{zhang2024video} & 70.9 \cite{zohar2024apolloexplorationvideounderstanding} & 1.70 \cite{yao2024minicpm} \\
+    \midrule
+    \modelname-7B & \textbf{64.5} (128f) & 58.6 (128f) & \textbf{65.3} (128f) & 67.9 (256f) & \textbf{1.82} (128f) \\
+    \bottomrule
+    \end{tabular}%
+    }
+    \caption{Evaluation results on long-video question answering benchmarks. We list the number of frames used for each benchmark during evaluating \modelname.}
+    \label{tab:results-long-video-qa}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/5.tex

@@ -0,0 +1,37 @@
+\begin{table}[h!]
+    \centering
+    \resizebox{\textwidth}{!}{%
+    \begin{tabular}{l|c|cc}
+    \toprule
+    \multirow{2}{*}{\textbf{Model}} & \textbf{VideoHallucer} \cite{wang2024videohallucer} & \multicolumn{2}{c}{\textbf{EventHallusion }\cite{zhang2024eventhallusion}} \\
+    \cline{2-4}
+    & Yes/No QA & Yes/No QA & Desc GPT \\
+    & Basic/Hallucinated/\textbf{Overall} & Entire/Interleave/Misleading/\textbf{Overall} & Entire/Interleave/Misleading/\textbf{Overall} \\
+    % \midrule
+    % Human & 90.0 / 88.8 / 85.0 & - & - \\
+    \midrule
+    \multicolumn{4}{l}{\textit{Proprietary models}}\\
+    GPT-4o \cite{gpt4o} & 75.1/74.2/53.3 & 65.8/90.7/92.2/84.1 & 34.9/54.9/83.2/56.2 \\
+    Gemini-1.5-Pro \cite{geminiteam2024gemini15unlockingmultimodal} & 83.6/42.3/37.8 & 70.2/77.7/96.1/80.2 & 38.5/40.9/80.0/49.6 \\
+    \midrule
+    \multicolumn{4}{l}{\textit{Open-Source models ($>$10B)}} \\
+    Qwen2-VL-72B \cite{qwen2vl} & 87.1/79.4/\underline{70.2} & 33.3/77.7/56.4/60.0 & 16.5/25.4/70.2/33.6 \\
+    LLaVA-OV-72B \cite{li2024llavanext} & 88.3/62.6/55.2 & 47.4/26.9/90.1/48.3 & 24.8/34.7/71.3/40.7 \\
+    LLaVA-Video-72B \cite{zhang2024video} & 88.2/73.5/64.6  & 57.9/11.9/96.0/45.6 & 32.1/35.8/75.5/44.2 \\
+    InternVL2.5-78B \cite{chen2024expanding} & 82.5/82.5/67.8 & 57.9/67.9/88.2/70.2 & 45.0/43.0/76.8/51.6 \\
+    Tarsier-34B \cite{wang2024tarsierrecipestrainingevaluating} & 84.8/80.0/67.7 & 49.1/92.7/69.6/74.8 & 38.5/40.4/83.2/50.1 \\
+    \midrule
+    \multicolumn{4}{l}{\textit{Open-Source models ($\leq$10B)}} \\
+    % PLLaVA-7B \cite{xu2024pllava} & 75.1/55.5/38.1 & \\
+    LLaVA-OV-7B \cite{li2024llavanext} & 81.1/69.6/53.8 & 46.5/67.4/86.1/66.2 & 22.0/26.4/73.4/36.4 \\
+    LLaVA-Video-7B \cite{zhang2024video} & 82.4/70.6/56.0 & 61.4/48.7/96.0/64.0 & 27.5/32.6/75.5/41.4 \\
+    Qwen2-VL-7B \cite{qwen2vl} & 85.0/70.8/59.3 & 35.1/94.3/57.4/68.6 & 14.7/16.1/67.0/27.8 \\
+    InternVL2.5-8B \cite{chen2024expanding} &72.7/78.3/53.6&46.5/69.2/90.2/68.2&23.9/20.7/60.0/31.0\\
+    Tarsier-7B \cite{wang2024tarsierrecipestrainingevaluating} & 76.4/60.8/41.4 & 43.9/82.4/79.4/70.9 & 35.8/29.5/72.6/41.6 \\
+    \midrule
+    \modelname-7B & 86.5/78.3/\textbf{67.0}&60.5/93.3/95.1/\underline{\textbf{84.6}}&54.6/53.1/93.7/\underline{\textbf{63.3}} \\
+    \bottomrule
+    \end{tabular}}
+    \caption{Evaluation results on hallucination benchmarks.}
+    \label{tab:video_hallucination}
+    \end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/6.tex

@@ -0,0 +1,30 @@
+\begin{table}[h]
+    \centering
+    \resizebox{0.8\textwidth}{!}{%
+    \begin{tabular}{l|cccccc}
+    \toprule
+        \multirow{2}{*}{\textbf{Model}} & \multicolumn{6}{c}{\textbf{E.T. Bench-Grounding} \cite{liu2024etbench}}\\
+        & TVG$_{F1}$ & EPM$_{F1}$ & TAL$_{F1}$ & EVS$_{F1}$ & VHD$_{F1}$ & \textbf{Mean$_{F1}$}\\
+    \midrule
+    \multicolumn{7}{l}{\textit{Proprietary models}} \\
+        GPT-4V \cite{gpt4v} & \color{lightgray}27.0 & \color{lightgray}1.8 & \color{lightgray}18.0 & \color{lightgray}\underline{28.6} & \color{lightgray}55.1 & \color{lightgray}26.1\\
+        GPT-4o \cite{gpt4o} & \color{lightgray}40.4 & \color{lightgray}4.5 & \color{lightgray}20.0 & \color{lightgray}17.6 & \color{lightgray}56.9 & \color{lightgray}27.9\\
+        Gemini-1.5-Flash \cite{geminiteam2024gemini15unlockingmultimodal} & \color{lightgray}\underline{43.9} & \color{lightgray}5.4 & \color{lightgray}27.0 & \color{lightgray}5.4 & \color{lightgray}60.8 & \color{lightgray}28.5\\
+        Gemini-1.5-Pro \cite{geminiteam2024gemini15unlockingmultimodal} & \color{lightgray}43.1  & \color{lightgray}6.2 & \color{lightgray}33.8 & \color{lightgray}7.9 & \color{lightgray}47.0 & \color{lightgray}27.6\\
+    \midrule
+    \multicolumn{7}{l}{\textit{Open-source models ($<$10B)}} \\
+       LITA \cite{huang2024lita} & 22.2 & 4.6 & 18.0 & 29.7 & 23.9 & 19.7 \\
+       VTG-LLM \cite{guo2024vtg} & 15.9 & 3.7 & 14.4 & 26.8 & 48.2 & 21.8 \\
+       % TimeChat 26.2 & 3.9 & 10.1 & 29.1 & 40.5 & 22.0\\
+       TimeChat \cite{Ren2023TimeChat} $^\dag$ & - & - & - & - & - & 24.3 \\
+       E.T. Chat \cite{liu2024etbench} $^\dag$ & 38.6 & 10.2 & 30.8 & \textbf{25.4} & 62.5 & 33.5 \\
+       Tarsier-7B \cite{wang2024tarsierrecipestrainingevaluating} $^\dag$ & 39.6 & 9.0 & 25.0 & \textbf{25.4} & 47.6 & 30.9 \\
+       Qwen2-VL-7B \cite{qwen2vl} $^\dag$ & \textbf{39.7} & 7.0 & 26.9 & 17.1 & \underline{\textbf{66.9}} & 33.5 \\
+    \midrule
+        Tarsier2-7B $^\dag$ & 38.4 & \underline{\textbf{11.0}} & \underline{\textbf{31.8}} & 19.4 & 66.8 & \underline{\textbf{35.5}} \\
+    \bottomrule
+    \end{tabular}
+    }
+    \caption{Evaluation results on E.T. Bench-Grounding. Results marked in gray are tested on a subset. $\dag$ denotes the model is fine-tuned on E.T. Instruct 164K.}
+    \label{tab:grounding}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/7.tex

@@ -0,0 +1,55 @@
+\begin{table}[h]
+    \centering
+    \setlength{\tabcolsep}{2pt} % 调整列间距
+    \scriptsize
+    \begin{minipage}{0.27\textwidth}
+        \centering
+        \begin{tabular}{l|c}
+            \toprule
+             \multirow{2}{*}{\textbf{Model}} & \textbf{EgoTaskQA} \\
+             & Exact Match \\
+            \midrule
+            Human & 80.0 \\
+            HCRN \cite{le2020hierarchical} & 42.2 \\
+            GF \cite{bai2024glance} & 44.3 \\
+            EgoVLPv2 \cite{pramanick2023egovlpv2} & 46.3 \\
+            \midrule
+            \modelname & \underline{\textbf{77.5}} \\
+            \bottomrule
+        \end{tabular}
+    \end{minipage}
+     \begin{minipage}{0.415\textwidth}
+        \centering
+        \begin{tabular}{l|c}
+            \toprule
+            \multirow{2}{*}{\textbf{Model}} & \textbf{RoboVQA} \\
+            & BLEU-1/2/3/4\\
+            \midrule
+            LLaMA-AdapterV2 \cite{gao2023llama} & 27.8/16.0/10.9/8.1 \\
+            LLaVA-OV-7B \cite{li2024llavanext} & 38.1/33.6/31.8/31.0 \\
+            RoboMamba \cite{liu2024robomamba} & 54.9/44.2/39.5/36.3 \\
+            MLCD \cite{an2025multi} & 73.2/66.4/60.6/56.6 \\
+            \midrule
+            \modelname & \underline{\textbf{77.1}}/\underline{\textbf{67.4}}/\underline{\textbf{61.5}}/\underline{\textbf{56.8}} \\
+            \bottomrule
+        \end{tabular}
+    \end{minipage}
+    \begin{minipage}{0.3\textwidth}
+        \centering
+        \begin{tabular}{l|c}
+            \toprule
+            \multirow{2}{*}{\textbf{Model}} & \textbf{OpenEQA} \\
+            & GPT-4\\
+            \midrule
+            Human & 86.8 \\
+            GPT-4V \cite{gpt4v} & 55.3 \\
+            Gemini-1.5-Pro \cite{geminiteam2024gemini15unlockingmultimodal} & 44.9 \\
+            MLCD \cite{an2025multi} & 48.8 \\
+            \midrule
+            \modelname & \underline{\textbf{58.7}} \\
+            \bottomrule
+        \end{tabular}
+    \end{minipage}
+    \caption{Evaluation results on embodied question-answering tasks, including EgoTaskQA, RoboVQA and OpenEQA.}
+    \label{table:evaluate-EgoTaskQA}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/8.tex

@@ -0,0 +1,21 @@
+\begin{table}[h]
+    \centering
+    \scriptsize
+    \setlength{\tabcolsep}{3pt} % 调整列间距
+    \resizebox{\textwidth}{!}{%
+    \begin{tabular}{l|ccc|cc|c}
+    \toprule
+    \multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Caption}} & \multicolumn{2}{c|}{\textbf{Video QA}} & \multirow{2}{*}{\textbf{Hallucination}} \\
+    & DREAM-1K & TempCompass-cg & Vinoground-Text & Short & Long & \\
+    \midrule
+    Tarsier1-7B & 34.6 & 55.3 & 29.8 & 45.6 & 46.3 & 56.3 \\
+    \midrule
+    \makecell[l]{Tarsier1-7B-Qwen\\ \quad\textit{upgrading model}} & 38.4 ($\uparrow$3.8) & 59.3 ($\uparrow$4.0) & 48.6 ($\uparrow$18.8) & 52.4 ($\uparrow$6.8) & 57.6 ($\uparrow$11.3) & 62.1 ($\uparrow$5.8) \\
+    \midrule
+    \makecell[l]{Tarsier2-7B\\ \quad\textit{upgrading model}+\textit{data}} & 40.8 ($\uparrow$6.2) & 60.1 ($\uparrow$4.8) & 60.2 ($\uparrow$30.4) & 55.3 ($\uparrow$9.7) & 64.1 ($\uparrow$17.8) & 63.5 ($\uparrow$7.2)\\
+    \bottomrule
+    \end{tabular}
+    }
+    \caption{Results of the ablation study for pre-training. Tarsier1-7b-Qwen stands for the model where the base model is upgraded to Qwen2-VL, while the pre-training dataset remains the same as Tarsier1. Tarsier2 is trained from Qwen2-VL with an expanded pre-training dataset, growing from 13 million in Tarsier1 to 40 million samples.}
+    \label{tab:pretrain_ab_vlm_and_data}
+\end{table}

workspace/01_Productivity_Flow/task_2_table_tex_download/gt/9.tex

@@ -0,0 +1,17 @@
+\begin{table}[t]
+    \centering
+    \setlength{\tabcolsep}{2pt} % 调整列间距
+    \resizebox{\textwidth}{!}{\begin{tabular}{l|ccc|cc|c}
+    \toprule
+    \multirow{2}{*}{\textbf{Model}} & \multicolumn{3}{c|}{\textbf{Caption}} & \multicolumn{2}{c|}{\textbf{Video QA}} & \multirow{2}{*}{\textbf{Hallucination}} \\
+    & DREAM-1K & TempCompass-cg & Vinoground-Text & Short & Long & \\
+    \midrule
+    \modelname-7B-SFT & 40.8 & 60.1 & 60.2 & 56.2 & 63.2 & 71.9 \\
+    \midrule
+    \quad \textit{w/o SFT} & 35.2 ($\downarrow$5.6) & 50.5 ($\downarrow$9.6) & 57.2 ($\downarrow$3.0) & 55.3 ($\downarrow$0.9) & 64.1 ($\uparrow$0.9) & 63.5 ($\downarrow$8.4) \\
+    \quad \textit{w/o grounding} & 37.4 ($\downarrow$3.4)  & 50.2 ($\downarrow$9.9)  & 60.6 ($\uparrow$0.4) & 55.9 ($\downarrow$0.3)  & 61.9 ($\downarrow$1.3) & 68.6 ($\downarrow$3.3)  \\
+    \bottomrule
+    \end{tabular}}
+    \caption{Ablation study of temporal grounding dataset during the SFT phase. \modelname-7B-SFT refers to the model after the SFT phase. \textit{w/o SFT} refers to the model after pre-training; \textit{w/o grounding} refers to the model fine-tinued without grounding information.}
+\label{tab:pretrain_ab_grounding}
+\end{table}

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workspace/01_Productivity_Flow/task_3_bibtex/gt/2203.02155.bib

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+@misc{ouyang2022traininglanguagemodelsfollow,
+      title={Training language models to follow instructions with human feedback}, 
+      author={Long Ouyang and Jeff Wu and Xu Jiang and Diogo Almeida and Carroll L. Wainwright and Pamela Mishkin and Chong Zhang and Sandhini Agarwal and Katarina Slama and Alex Ray and John Schulman and Jacob Hilton and Fraser Kelton and Luke Miller and Maddie Simens and Amanda Askell and Peter Welinder and Paul Christiano and Jan Leike and Ryan Lowe},
+      year={2022},
+      eprint={2203.02155},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2203.02155}, 
+}

workspace/01_Productivity_Flow/task_3_bibtex/gt/2301.12597.bib

@@ -0,0 +1,9 @@
+@misc{li2023blip2bootstrappinglanguageimagepretraining,
+      title={BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models}, 
+      author={Junnan Li and Dongxu Li and Silvio Savarese and Steven Hoi},
+      year={2023},
+      eprint={2301.12597},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2301.12597}, 
+}