Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Pourreza, Reza"'
Autor:
Panchal, Sunny, Bhattacharyya, Apratim, Berger, Guillaume, Mercier, Antoine, Bohm, Cornelius, Dietrichkeit, Florian, Pourreza, Reza, Li, Xuanlin, Madan, Pulkit, Lee, Mingu, Todorovich, Mark, Bax, Ingo, Memisevic, Roland
Tasks at the intersection of vision and language have had a profound impact in advancing the capabilities of vision-language models such as dialog-based assistants. However, models trained on existing tasks are largely limited to turn-based interacti
Externí odkaz:
http://arxiv.org/abs/2407.08101
Autor:
Ling, Zhan, Fang, Yunhao, Li, Xuanlin, Mu, Tongzhou, Lee, Mingu, Pourreza, Reza, Memisevic, Roland, Su, Hao
Large Language Models (LLMs) have achieved tremendous progress, yet they still often struggle with challenging reasoning problems. Current approaches address this challenge by sampling or searching detailed and low-level reasoning chains. However, th
Externí odkaz:
http://arxiv.org/abs/2311.00694
Autor:
Pourreza, Reza, Bhattacharyya, Apratim, Panchal, Sunny, Lee, Mingu, Madan, Pulkit, Memisevic, Roland
Large language models (LLMs) have made tremendous progress in natural language understanding and they have also been successfully adopted in other domains such as computer vision, robotics, reinforcement learning, etc. In this work, we apply LLMs to
Externí odkaz:
http://arxiv.org/abs/2308.08520
Autor:
Bhattacharyya, Apratim, Panchal, Sunny, Lee, Mingu, Pourreza, Reza, Madan, Pulkit, Memisevic, Roland
Multi-modal language models (LM) have recently shown promising performance in high-level reasoning tasks on videos. However, existing methods still fall short in tasks like causal or compositional spatiotemporal reasoning over actions, in which model
Externí odkaz:
http://arxiv.org/abs/2306.17778
Publikováno v:
Picture Coding Symposium (PCS), San Jose, CA, USA, 2022, pp. 379-383
Neural networks (NN) can improve standard video compression by pre- and post-processing the encoded video. For optimal NN training, the standard codec needs to be replaced with a codec proxy that can provide derivatives of estimated bit-rate and dist
Externí odkaz:
http://arxiv.org/abs/2301.09776
Publikováno v:
IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 2022, pp. 661-665
Neural-based image and video codecs are significantly more power-efficient when weights and activations are quantized to low-precision integers. While there are general-purpose techniques for reducing quantization effects, large losses can occur when
Externí odkaz:
http://arxiv.org/abs/2301.08752
In video compression, coding efficiency is improved by reusing pixels from previously decoded frames via motion and residual compensation. We define two levels of hierarchical redundancy in video frames: 1) first-order: redundancy in pixel space, i.e
Externí odkaz:
http://arxiv.org/abs/2208.04303
Autor:
Le, Hoang, Zhang, Liang, Said, Amir, Sautiere, Guillaume, Yang, Yang, Shrestha, Pranav, Yin, Fei, Pourreza, Reza, Wiggers, Auke
Realizing the potential of neural video codecs on mobile devices is a big technological challenge due to the computational complexity of deep networks and the power-constrained mobile hardware. We demonstrate practical feasibility by leveraging Qualc
Externí odkaz:
http://arxiv.org/abs/2207.08338
Autor:
van Rozendaal, Ties, Brehmer, Johann, Zhang, Yunfan, Pourreza, Reza, Wiggers, Auke, Cohen, Taco S.
We introduce a video compression algorithm based on instance-adaptive learning. On each video sequence to be transmitted, we finetune a pretrained compression model. The optimal parameters are transmitted to the receiver along with the latent code. B
Externí odkaz:
http://arxiv.org/abs/2111.10302
Autor:
Singh, Ankitesh K., Egilmez, Hilmi E., Pourreza, Reza, Coban, Muhammed, Karczewicz, Marta, Cohen, Taco S.
Most of the existing deep learning based end-to-end video coding (DLEC) architectures are designed specifically for RGB color format, yet the video coding standards, including H.264/AVC, H.265/HEVC and H.266/VVC developed over past few decades, have
Externí odkaz:
http://arxiv.org/abs/2104.00807