Search Results for author:
Found 10 papers, 8 papers with code
Optimal Embedding Dimension for Sparse Subspace Embeddings
We use this to construct the first oblivious subspace embedding with $O(d)$ embedding dimension that can be applied faster than current matrix multiplication time, and to obtain an optimal single-pass algorithm for least squares regression.
Understanding Dark Scenes by Contrasting Multi-Modal Observations
Experiments show that our approach can effectively enhance dark scene understanding based on multi-modal images with limited semantics by shaping semantic-discriminative feature spaces.
Ranked #1 on
Semantic Segmentation
on LLRGBD-synthetic
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
To facilitate the development of cooperative perception, we present V2V4Real, the first large-scale real-world multi-modal dataset for V2V perception.
Bridging the Domain Gap for Multi-Agent Perception
Existing multi-agent perception algorithms usually select to share deep neural features extracted from raw sensing data between agents, achieving a trade-off between accuracy and communication bandwidth limit.
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution
Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high-resolution (HR) guide images from different modalities are available.
Pik-Fix: Restoring and Colorizing Old Photos
Our proposed framework consists of three modules: a restoration sub-network that conducts restoration from degradations, a similarity network that performs color histogram matching and color transfer, and a colorization subnet that learns to predict the chroma elements of images conditioned on chromatic reference signals.
ROMNet: Renovate the Old Memories
Renovating the memories in old photos is an intriguing research topic in computer vision fields.
Learnable Lookup Table for Neural Network Quantization
Since a linear quantizer (i. e., round(*) function) cannot well fit the bell-shaped distributions of weights and activations, many existing methods use pre-defined functions (e. g., exponential function) with learnable parameters to build the quantizer for joint optimization.
Unsupervised Degradation Representation Learning for Blind Super-Resolution
In this paper, we propose an unsupervised degradation representation learning scheme for blind SR without explicit degradation estimation.
Exploring Sparsity in Image Super-Resolution for Efficient Inference
Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation.
