10 citations · 24 across the 9 of their papers we have counts for
15 papers
How Much Can We Prune? Exploring the Performance Limits of Visual Token Pruning
Janghoon Cho, Shubhankar Borse, Reza Pourreza +4
Visual token pruning is increasingly used to reduce the memory and inference costs of vision-language models. Existing work has largely focused on designing method-specific selecto…
Alleviating Hallucination in Reasoning Tasks with Training-Free Uncertainty-Guided Steering
Litian Liu, Qiqi Hou, Yubing Jian +5
Recent work on hallucination detection in large language models has shown that, for a fixed pre-trained model and reasoning task, it is possible to estimate the model's confidence…
Unleashing the Creative Mind: Language Model As Hierarchical Policy For Improved Exploration on Challenging Problem Solving
Zhan Ling, Yunhao Fang, Xuanlin Li +5
Large Language Models (LLMs) have achieved tremendous progress, yet they still often struggle with challenging reasoning problems. Current approaches address this challenge by samp…
Painter: Teaching Auto-regressive Language Models to Draw Sketches
Reza Pourreza, Apratim Bhattacharyya, Sunny Panchal +3
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,…
Look, Remember and Reason: Grounded reasoning in videos with language models
Apratim Bhattacharyya, Sunny Panchal, Mingu Lee +3
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…
Differentiable bit-rate estimation for neural-based video codec enhancement
Amir Said, Manish Kumar Singh, Reza Pourreza
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 c…
Optimized learned entropy coding parameters for practical neural-based image and video compression
Amir Said, Reza Pourreza, Hoang Le
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 tec…
Boosting neural video codecs by exploiting hierarchical redundancy
Reza Pourreza, Hoang Le, Amir Said +2
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 redu…
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices
Hoang Le, Liang Zhang, Amir Said +6
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 mob…
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set
Ties van Rozendaal, Johann Brehmer, Yunfan Zhang +3
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 pa…
A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space
Ankitesh K. Singh, Hilmi E. Egilmez, Reza Pourreza +3
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.26…
Extending Neural P-frame Codecs for B-frame Coding
Reza Pourreza, Taco S Cohen
While most neural video codecs address P-frame coding (predicting each frame from past ones), in this paper we address B-frame compression (predicting frames using both past and fu…
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning
Dana Kianfar, Auke Wiggers, Amir Said +2
Rate-Distortion Optimized Quantization (RDOQ) has played an important role in the coding performance of recent video compression standards such as H.264/AVC, H.265/HEVC, VP9 and AV…
Feedback Recurrent Autoencoder for Video Compression
Adam Golinski, Reza Pourreza, Yang Yang +2
Recent advances in deep generative modeling have enabled efficient modeling of high dimensional data distributions and opened up a new horizon for solving data compression problems…
Adversarial Distortion for Learned Video Compression
Vijay Veerabadran, Reza Pourreza, Amirhossein Habibian +1
In this paper, we present a novel adversarial lossy video compression model. At extremely low bit-rates, standard video coding schemes suffer from unpleasant reconstruction artifac…