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Works90 from public data
- Deblurring by Realistic Blurring494
This paper proposes a new method which combines two GAN models, i.e., a learning-to-Blur GAN (BGAN) and learning- to-DeBlurGAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.
- Hierarchical Neural Architecture Search for Deep Stereo Matching438
This paper proposes the first end-to-end hierarchical NAS framework for deep stereo matching by incorporating task-specific human knowledge into the neural architecture search framework and optimizing the architectures of the entire pipeline jointly.
- MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention236
Experiments on standard benchmarks show that the MiniMax-M1 models are comparable or superior to strong open-weight models such as the original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex software engineering, tool utilization, and long-context tasks.
- Positive Sample Propagation along the Audio-Visual Event Line149
A new positive sample propagation (PSP) module is proposed to discover and exploit the closely related audio-visual pairs by evaluating the relationship within every possible pair by constructing an all-pair similarity map between each audio and visual segment.
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- RGB-D Saliency Detection via Cascaded Mutual Information Minimization131
This paper introduces a novel multistage cascaded learning framework via mutual information minimization to explicitly model the multi-modal information between RGB image and depth data.
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- Implicit Motion Handling for Video Camouflaged Object Detection111
A new video camouflaged object detection framework that can exploit both short-term dynamics and long-term temporal consistency to detect camouflaged objects from video frames is proposed and unifies motion estimation and object segmentation within a single optimization framework.
- Audio-Visual Segmentation with Semantics104
A new baseline method is proposed that uses a temporal pixel-wise audio-visual interaction module to inject audio semantics as guidance for the visual segmentation process, and a regularization loss is designed to encourage audio-visual mapping during training.
- ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring92
Quantitative and qualitative experiments show that the proposed implicit method to learn spatial correspondence among blurry frames in the feature space performs favorably on both datasets against previous state-of-the-art methods, confirming the benefit of modeling all-range spatial correspondence for video deblurring.
- Open-World Stereo Video Matching with Deep RNN79
A novel Recurrent Neural Network (RNN) that takes a continuous stereo video as input, and directly predicts a depth-map at each frame without a pre-training process, and without the need of ground-truth depth-maps as supervision.
- Learning Audio-Visual Source Localization via False Negative Aware Contrastive Learning76
This work proposes a new learning strategy named False Negative Aware Contrastive (FNAC), which utilizes the intra-modal similarities to identify potentially similar samples and construct corresponding adjacency matrices to guide contrastive learning.
- Noise-Aware Unsupervised Deep Lidar-Stereo Fusion75
LidarStereoNet is presented, the first unsupervised Lidar-stereo fusion network, which can be trained in an end-to-end manner without the need of ground truth depth maps, and is proposed to incorporate the piecewise planar model into the network learning to further constrain depths to conform to the underlying 3D geometry.
- Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes75
Experimental results on various benchmarking datasets show that the Deep Epipolar Flow method achieves competitive performance compared with supervised methods and outperforms state-of-the-art unsupervised deep-learning methods.
- Deep Two-View Structure-from-Motion Revisited69
This work proposes to revisit the problem of deep two-view SfM by leveraging the well-posedness of the classic pipeline by developing a scale-invariant depth estimation network that leverages epipolar geometry to reduce the search space, refine the dense correspondences, and estimate relative depth maps.
- Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models64
Lightning Attention-2 is presented, the first linear attention implementation that enables linear attention to realize its theoretical computational benefits and retains consistent training and inference speed regardless of input sequence length and is significantly faster than other attention mechanisms.
- Multimodal Variational Auto-encoder based Audio-Visual Segmentation64
An Explicit Conditional Multimodal Variational Auto-Encoder for audio-visual segmentation (AVS), aiming to segment sound sources in the video sequence, with a new state-of-the-art performance leap on the challenging MS3 subset for multiple sound source segmentation.
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- Vicinity Vision Transformer58
Vicinity Attention, which is a type of linear attention that integrates 2D locality that achieves state-of-the-art image classification accuracy with 50% fewer parameters than previous approaches, is proposed.
- Contrastive Conditional Latent Diffusion for Audio-Visual Segmentation55
A contrastive conditional latent diffusion model for audio-visual segmentation (AVS) is proposed to thoroughly investigate the impact of audio, where the correlation between audio and the final segmentation map is modeled to guarantee the strong correlation between them.
- Toeplitz Neural Network for Sequence Modeling55
A lightweight sub-network called relative position encoder is proposed to generate relative position coefficients with a fixed budget of parameters, enabling the proposed Toeplitz neural network to deal with varying sequence lengths.
- TAVGBench: Benchmarking Text to Audible-Video Generation53
A baseline model for TAVG is presented called TAVDiffusion, which uses a two-stream latent diffusion model to provide a fundamental starting point for further research in this area and proposes an automatic annotation pipeline to ensure each audible video has detailed descriptions for both its audio and video contents.
- Advancing Weakly-Supervised Audio-Visual Video Parsing via Segment-Wise Pseudo Labeling49
A new pseudo label generation strategy that can explicitly assign labels to each video segment by utilizing prior knowledge learned from the open world is proposed and achieves state-of-the-art video parsing performance on all types of event parsing, i.e., audio event, visual event, and audio-visual event.
- Towards Open-Vocabulary Audio-Visual Event Localization46
This paper introduces the Open-Vocabulary Audio-Visual Event Localization (OV-AVEL) problem, which requires localizing audio-visual events and predicting explicit categories for both seen and unseen test data at inference, and investigates two baseline approaches, one training-free and one using a further fine-tuning paradigm.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. 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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