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Found 14 papers, 3 papers with code
ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Based on W2*A8 quantization configuration on LLaMA-7B model, it achieved a WikiText2 perplexity of 7. 59 (2. 17$\downarrow $ vs 9. 76 in AffineQuant).
Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models
On the flip side, while there are many compact models tailored for edge devices that can reduce these demands, they often compromise on semantic integrity and visual quality when compared to full-sized SDMs.
FoldGPT: Simple and Effective Large Language Model Compression Scheme
Based on this observation, we propose an efficient model volume compression strategy, termed FoldGPT, which combines block removal and block parameter sharing. This strategy consists of three parts: (1) Based on the learnable gating parameters, we determine the block importance ranking while modeling the coupling effect between blocks.
PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast Training and Rendering
VDB takes both the advantages of sparse and dense volumes for compact data representation and efficient data access, being a promising data structure for NeRF data interpolation and ray marching.
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues
Recently, significant progress has been made in single-view depth estimation thanks to increasingly large and diverse depth datasets.
Effective Label Propagation for Discriminative Semi-Supervised Domain Adaptation
For intra-domain propagation, we propose an effective self-training strategy to mitigate the noises in pseudo-labeled target domain data and improve the feature discriminability in the target domain.
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning
The TargetNet module is a neural network for solving a specific task and the MetaNet module aims at learning to generate functional weights for TargetNet by observing training samples.
Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment
Aggregation structures with explicit information, such as image attributes and scene semantics, are effective and popular for intelligent systems for assessing aesthetics of visual data.
Ranked #1 on
Aesthetics Quality Assessment
on AVA
Improving Image Restoration with Soft-Rounding
In this work, we describe an effective and efficient approach to incorporate the knowledge of distinct pixel values of the pristine images into the general regularized least squares restoration framework.
UniHIST: A Unified Framework for Image Restoration With Marginal Histogram Constraints
Marginal histograms provide valuable information for various computer vision problems.
Image Retargeting by Content-Aware Synthesis
We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators.
Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves
In this work, we focus on unsupervised ranking from multi-attribute data which is also common in evaluation tasks.
Segment-Tree Based Cost Aggregation for Stereo Matching
Instead of employing the minimum spanning tree (MST) and its variants, a new tree structure, "Segment-Tree", is proposed for non-local matching cost aggregation.
Principal Observation Ray Calibration for Tiled-Lens-Array Integral Imaging Display
Based on this, 3D image artifacts are shown to be effectively removed in a test TLA-IID with challenging misalignments.
