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- Shuicheng YanSuggested from co-authorship
- Wei LiuSuggested from co-authorship
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Works94 from public data
- Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation627
It is found that varying dilation rates can effectively enlarge the receptive fields of convolutional kernels and more importantly transfer the surrounding discriminative information to non-discriminative object regions, promoting the emergence of these regions in the object localization maps.
- A Sufficient Condition for Convergences of Adam and RMSProp470
An alternative easy-to-check sufficient condition is introduced, which merely depends on the parameters of the base learning rate and combinations of historical second-order moments, to guarantee the global convergence of generic Adam/RMSProp for solving large-scale non-convex stochastic optimization.
- Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms409
This diagnostic accuracy study evaluates whether artificial intelligence can overcome human mammography interpretation limits with a rigorous, unbiased evaluation of machine learning algorithms.
- Central Similarity Quantization for Efficient Image and Video Retrieval392
The Central Similarity Quantization (CSQ) is proposed, a new similarity metric with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy.
- Temporally Grounding Natural Sentence in Video345
An effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences using a novel Temporal GroundNet to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence.
- PromptDet: Towards Open-Vocabulary Detection Using Uncurated Images245
The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations, and the proposed detector, termed as PromptDet, shows superior performance over existing approaches with fewer additional training images andzero manual annotations whatsoever.
- Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation237
A novel joint Task-Recursive Learning (TRL) framework for the closing-loop semantic segmentation and monocular depth estimation tasks and propagate previous learning experiences on both tasks into the next network evolution by explicitly concatenating previous responses.
- Deep Self-Taught Learning for Weakly Supervised Object Localization207
A deep self-taught learning approach is proposed, which makes the detector learn the object-level features reliable for acquiring tight positive samples and afterwards re-train itself based on them, which progressively improves its detection ability and localizes more informative positive samples.
- NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing197
A novel Representative Region NMS (R2NMS) approach leveraging the less occluded visible parts, effectively removing the redundant boxes without bringing in many false positives is proposed.
- Multi-View Image Generation from a Single-View193
This paper proposes a novel image generation model termed VariGANs, which combines the merits of the variational inference and the Generative Adversarial Networks (GANs), and generates the target image in a coarse-to-fine manner instead of a single pass which suffers from severe artifacts.
- Video-Based Person Re-Identification With Accumulative Motion Context181
The experimental results demonstrate that the proposed AMOC network outperforms state-of-the-arts for video-based re-identification significantly and confirm the advantage of exploiting long-range motion context forVideo-based person re-Identification, validating the motivation evidently.
- Policy Optimization with Demonstrations180
It is shown that POfD induces implicit dynamic reward shaping and brings provable benefits for policy improvement, and can be combined with policy gradient methods to produce state-of-the-art results, as demonstrated experimentally on a range of popular benchmark sparse-reward tasks.
- Neural Person Search Machines171
Evaluations on two benchmark datasets have demonstrated that the Neural Person Search Machines (NPSM) developed can outperform current state-of-the-arts in both mAP and top-1 evaluation protocols.
- MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection152
A novel framework with better utilization of the depth information and fine-grained cross-modal interaction between LiDAR and camera is proposed, which consists of two important components and achieves state-of-the-art results on both 3D object detection and tracking tasks without using test-time-augmentation and ensemble techniques.
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- Video Scene Parsing with Predictive Feature Learning117
It is experimentally proved that the learned predictive features in the model are able to significantly enhance the video parsing performance by combining with the standard image parsing network.
- MTL-NAS: Task-Agnostic Neural Architecture Search Towards General-Purpose Multi-Task Learning113
A novel single-shot gradient-based search algorithm that closes the performance gap between the searched architectures and the final evaluation architecture and achieves a single model, which can be directly used for evaluation without (re-)training from scratch.
- LLaVA-MoLE: Sparse Mixture of LoRA Experts for Mitigating Data Conflicts in Instruction Finetuning MLLMs107
Extensive experiments proved that LLaVA-MoLE effectively mitigates the data conflict issue when mixing multiple distinct instruction datasets with various configurations, and achieves consistent performance gains over the strong plain-LoRA baselines.
- Localizing Natural Language in Videos107
A localizing network (LNet), working in an end-to-end fashion, to tackle the NLVL task, which first match the natural sentence and video sequence by cross-gated attended recurrent networks to exploit their fine-grained interactions and generate a sentence-aware video representation.
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- Learning to segment with image-level annotations99
This paper explores a more challenging problem by learning to segment under image-level annotations by incorporating the hypotheses-aware classification and cross-image contextual refinement in a supervised manner, and proposes two network training strategies for achieving good segmentation performance.
- Left-Right Comparative Recurrent Model for Stereo Matching89
A novel left- right comparative recurrent model to perform left-right consistency checking jointly with disparity estimation and a soft attention mechanism is introduced, which employs the learned error maps for better guiding the model to selectively focus on refining the unreliable regions at the next recurrent step.
- Modular Generative Adversarial Networks82
ModularGAN consists of several reusable and composable modules that carry on different functions that can be trained simultaneously, leveraging data from all domains, and then combined to construct specific GAN networks at test time, according to the specific image translation task.
- TimeMarker: A Versatile Video-LLM for Long and Short Video Understanding with Superior Temporal Localization Ability81
TimeMarker is introduced, a versatile Video-LLM designed for high-quality dialogue based on video content, emphasizing temporal localization, and achieves state-of-the-art performance across multiple benchmarks, excelling in both short and long video categories.
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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 2026-10-14. 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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