Omkar2118/datasets huggingface dataset:
Facebook MSC
Full name: Facebook MULTI-SESSION CHAT

Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context. In contrast, the long-term conversation setting has hardly been studied. In this work we collect and release a humanhuman dataset consisting of multiple chat sessions whereby the speaking partners learn about each other’s interests and discuss the things they have learnt from past sessions. We show how existing models trained on existing datasets perform poorly in this long-term conversation setting in both automatic and human evaluations, and we study long-context models that can perform much better. In particular, we find retrieval-augmented methods and methods with an ability to summarize and recall previous conversations outperform the standard encoder-decoder architectures currently considered state of the art.

Introducing paper: Beyond Goldfish Memory: Long-Term Open-Domain Conversation
DIBCO 2009

DIBCO 2009 is the first International Document Image Binarization Contest organized in the context of ICDAR 2009 conference. The general objective of the contest is to identify current advances in document image binarization using established evaluation performance measures.
H-DIBCO 2010

H-DIBCO 2010 is the International Document Image Binarization Contest which is dedicated to handwritten document images organized in conjunction with ICFHR 2010 conference. The general objective of the contest is to identify current advances in handwritten document image binarization using meaningful evaluation performance measures.
IISc VEED
Full name: Indian Institute of Science Virtual Environment Exploration Database of Static Scenes

**IISc VEED** consists of 200 diverse indoor and outdoor scenes (see samples below). The videos are rendered with blender and the blend files are obtained for the scenes mainly from blendswap and turbosquid. 4 different camera trajectories are added to each scene and thus we have a total of 800 videos. The videos are rendered at full HD resolution (1920 x 1080) and at 30fps and contain 12 frames each.

Source: [Revealing Disocclusions in Temporal View Synthesis through Infilling Vector Prediction](https://nagabhushansn95.github.io/publications/2021/ivp.html#database)

Image Source: [https://nagabhushansn95.github.io/publications/2021/ivp.html#database](https://nagabhushansn95.github.io/publications/2021/ivp.html#database)

Introducing paper: Revealing Disocclusions in Temporal View Synthesis through Infilling Vector Prediction