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Found 7 papers, 1 papers with code
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
We will be releasing all of our model weights, captioning and fine-tuning data, and source code in the near future.
ModEFormer: Modality-Preserving Embedding for Audio-Video Synchronization using Transformers
Lack of audio-video synchronization is a common problem during television broadcasts and video conferencing, leading to an unsatisfactory viewing experience.
SIDGAN: High-Resolution Dubbed Video Generation via Shift-Invariant Learning
Dubbed video generation aims to accurately synchronize mouth movements of a given facial video with driving audio while preserving identity and scene-specific visual dynamics, such as head pose and lighting.
ASAP-NMS: Accelerating Non-Maximum Suppression Using Spatially Aware Priors
The widely adopted sequential variant of Non Maximum Suppression (or Greedy-NMS) is a crucial module for object-detection pipelines.
RSO: A Gradient Free Sampling Based Approach For Training Deep Neural Networks
To this end, RSO adds a perturbation to a weight in a deep neural network and tests if it reduces the loss on a mini-batch.
Semantic Segmentation with Scarce Data
Semantic segmentation is a challenging vision problem that usually necessitates the collection of large amounts of finely annotated data, which is often quite expensive to obtain.
Recurrent neural networks based Indic word-wise script identification using character-wise training
Raw offline data lacks the temporal information available in online data and required for prediction using models trained with online data.
