4D LUT: Learnable Context-Aware 4D Lookup Table for Image Enhancement.

Image enhancement aims at improving the aesthetic visual quality of photos by retouching the color and tone, and is an essential technology for professional digital photography. Recent years deep learning-based image enhancement algorithms have achieved promising performance and attracted increasing popularity. However, typical efforts attempt to construct a uniform enhancer for all pixels' color transformation.

TTVFI: Learning Trajectory-Aware Transformer for Video Frame Interpolation.

Video frame interpolation (VFI) aims to synthesize an intermediate frame between two consecutive frames. State-of-the-art approaches usually adopt a two-step solution, which includes 1) generating locally-warped pixels by calculating the optical flow based on pre-defined motion patterns (e.g.

Aggregated Contextual Transformations for High-Resolution Image Inpainting.

Image inpainting that completes large free-form missing regions in images is a promising yet challenging task. State-of-the-art approaches have achieved significant progress by taking advantage of generative adversarial networks (GAN). However, these approaches can suffer from generating distorted structures and blurry textures in high-resolution images (e.

Cyclic Differentiable Architecture Search.

Differentiable ARchiTecture Search, i.e., DARTS, has drawn great attention in neural architecture search.

Multi-Scale 2D Temporal Adjacency Networks for Moment Localization With Natural Language.

We address the problem of retrieving a specific moment from an untrimmed video by natural language. It is a challenging problem because a target moment may take place in the context of other temporal moments in the untrimmed video. Existing methods cannot tackle this challenge well since they do not fully consider the temporal contexts between temporal moments.

Reference-Based Defect Detection Network.

The defect detection task can be regarded as a realistic scenario of object detection in the computer vision field and it is widely used in the industrial field. Directly applying vanilla object detector to defect detection task can achieve promising results, while there still exists challenging issues that have not been solved. The first issue is the texture shift which means a trained defect detector model will be easily affected by unseen texture, and the second issue is partial visual confusion which indicates that a partial defect box is visually similar with a complete box.

Revisiting Anchor Mechanisms for Temporal Action Localization.

Most of the current action localization methods follow an anchor-based pipeline: depicting action instances by pre-defined anchors, learning to select the anchors closest to the ground truth, and predicting the confidence of anchors with refinements. Pre-defined anchors set prior about the location and duration for action instances, which facilitates the localization for common action instances but limits the flexibility for tackling action instances with drastic varieties, especially for extremely short or extremely long ones. To address this problem, this paper proposes a novel anchor-free action localization module that assists action localization by temporal points.

Learning Rich Part Hierarchies with Progressive Attention Networks for Fine-Grained Image Recognition.

We investigate the localization of subtle yet discriminative parts for fine-grained image recognition. Based on the observation that such parts typically exist within a hierarchical structure (e.g.

Microstructural change of degummed Bombyx mori silk: an in situ stretching wide-angle X-ray-scattering study.

The microstructural change of degummed Bombyx mori silk was examined by in situ wide-angle X-ray-scattering (WAXS) with applied stretching force. WAXS patterns confirmed that the crystalline and amorphous regions coexist in the silk fibers. The crystallites with β-sheet structure have an orthorhombic unit cell with lattice parameters: a=9.