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- Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels664
The proposed Q-Align achieves state-of-the-art performance on image quality assessment (IQA), image aesthetic assessment (IAA), as well as video quality assessment (VQA) tasks under the original LMM structure and unify the three tasks into one model, termed the OneAlign.
- Cascade Residual Learning: A Two-Stage Convolutional Neural Network for Stereo Matching502
A novel cascade CNN architecture composing of two stages that advances the recently proposed DispNet by equipping it with extra up-convolution modules, leading to disparity images with more details, and shows that residual learning provides more effective refinement.
- GRNet: Gridding Residual Network for Dense Point Cloud Completion468
This work devise two novel differentiable layers, named Gridding and Gridding Reverse, to convert between point clouds and 3D grids without losing structural information, and presents the differentiable Cubic Feature Sampling layer to extract features of neighboring points, which preserves context information.
- Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives463
The Disentangled Objective Video Quality Evaluator (DOVER) is proposed, the first approach to provide reliable clear-cut quality evaluations from a single aesthetic or technical perspective, and the first approach to provide reliable clear-cut quality evaluations from a single aesthetic or technical perspective.
- TOPIQ: A Top-Down Approach From Semantics to Distortions for Image Quality Assessment423
This work proposes a top-down approach that uses high-level semantics to guide the IQA network to focus on semantically important local distortion regions, named as TOPIQ, and designs a heuristic coarse-to-fine network that leverages multi-scale features and progressively propagates multi-level semantic information to low-level representations in a top-down manner.
- Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision309
Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment, confirms that MLLMs possess preliminary low-level visual skills.
- Accurate Single Stage Detector Using Recurrent Rolling Convolution297
A novel single stage end-to-end trainable object detection network is proposed by introducing Recurrent Rolling Convolution (RRC) architecture over multi-scale feature maps to construct object classifiers and bounding box regressors which are deep in context.
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- Q-Instruct: Improving Low-Level Visual Abilities for Multi-Modality Foundation Models218
The first dataset consisting of human natural language feedback on low-level vision, and a GPT-participated transformation to convert these feedbacks into a rich set of 200K instruction-response pairs, termed Q-Instruct are collected.
- Single View Stereo Matching209
This paper shows for the first time that the monocular depth estimation problem can be reformulated as two sub-problems, a view synthesis procedure followed by stereo matching, with two intriguing properties, namely i) geometrical constraints can be explicitly imposed during inference; ii) demand on labelled depth data can be greatly alleviated.
- Shepard convolutional neural networks190
This paper draws on Shepard interpolation and design Shepard Convolutional Neural Networks (ShCNN) which efficiently realizes end-to-end trainable TVI operators in the network and shows that by adding only a few feature maps in the new Shepard layers, the network is able to achieve stronger results than a much deeper architecture.
- Efficient Regional Memory Network for Video Object Segmentation183
The proposed RM-Net effectively alleviates the ambiguity of similar objects in both memory and query frames, which allows the information to be passed from the regional memory to the query region efficiently and effectively.
- FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting180
This work proposes FuseFormer, a Transformer model designed for video inpainting via fine-grained feature fusion based on novel Soft Split and Soft Composition operations, which surpasses state-of-the-art methods in both quantitative and qualitative evaluations.
- Polarized Reflection Removal With Perfect Alignment in the Wild152
This work identifies the misalignment issues of existing reflection removal datasets where the collected reflection-free images are not perfectly aligned with input mixed images due to glass refraction and proposes a polarized reflection removal model with a two-stage architecture.
- LSTM Pose Machines148
It is shown that if the weight sharing scheme is imposed to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN), which decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos.
- Deep Animation Video Interpolation in the Wild127
This work formally defines and study the animation video interpolation problem for the first time, and proposes an effective framework, AnimeInterp, with two dedicated modules in a coarse-to-fine manner, which outperforms existing state-of-the-art interpolation methods for animation videos.
- Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement124
This work explicitly model the relationships of different image locations with an affinity layer and combine absolute and relative features in an end-to-end network to obtain global understanding of the image depth and exploit residual learning to help depth refinement.
- Towards Real Scene Super-Resolution With Raw Images122
A new pipeline to generate realistic training data by simulating the imaging process of digital cameras is proposed and a dual convolutional neural network is developed to exploit the originally captured radiance information in raw images to recover fine details and clear structures.
- Look, Listen and Learn — A Multimodal LSTM for Speaker Identification115
A novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input is described which outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.
- Enhanced Quadratic Video Interpolation100
This work improves the performance of QVI from three facets and proposes an enhanced quadratic video interpolation (EQVI) model that won the first place in the AIM2020 Video Temporal Super-Resolution Challenge and devise a novel multi-scale fusion network (MS-Fusion) which can be regarded as a learnable augmentation process.
- Neighbourhood Representative Sampling for Efficient End-to-End Video Quality Assessment99
This work proposes a unified scheme, spatial-temporal grid mini-cube sampling (St-GMS), and the resultant samples are named fragments, a network architecture tailored specifically for fragments that achieves up to 1612× efficiency than the existing state-of-the-art VQA benchmarks.
- DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment86
The proposed Temporal Distortion-Content Transformers for Video Quality Assessment (DisCoVQA) reaches state-of-the-art performance on several VQA benchmarks without any extra pre-training datasets and up to 10% better generalization ability than existing methods.
- Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach80
The MaxVQA is proposed, a language-prompted VQA approach that modifies vision-language foundation model CLIP to better capture important quality issues as observed in the authors' analyses.
- Deep Surface Normal Estimation With Hierarchical RGB-D Fusion79
A hierarchical fusion network with adaptive feature re-weighting is proposed for surface normal estimation from a single RGB-D image, outperforming state-of-the-art normal estimation schemes.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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