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Claim this profileWorks103 from public data
- Cost-Effective Active Learning for Deep Image Classification756
This paper proposes a novel active learning (AL) framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner and incorporates deep convolutional neural networks into AL.
- DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images446
A strong baseline is proposed, called Match R- CNN, which builds upon Mask R-CNN to solve the above four tasks in an end-to-end manner to address issues of DeepFashion2.
- Shallow Attention Network for Polyp Segmentation443
The Shallow Attention Network (SANet) is proposed, which design the color exchange operation to decouple the image contents and colors, and force the model to focus more on the target shape and structure, and the shallow attention module to filter out the background noise of shallow features.
- Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion400
A novel sparse LiDAR point cloud semantic segmentation framework assisted by learned contextual shape priors is proposed, which inherently improves SS optimization through fully end-to-end training.
- Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content347
This work proposes a novel visual try-on network, namely Adaptive Content Generating and Preserving Network (ACGPN), which can generate photo-realistic images with much better perceptual quality and richer fine-details.
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- Parser-Free Virtual Try-on via Distilling Appearance Flows267
This work proposes a novel approach, "teacher-tutor-student" knowledge distillation, which is able to produce highly photo-realistic images without human parsing, possessing several appealing advantages compared to prior arts.
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- End-to-End Dense Video Captioning with Parallel Decoding260
Without bells and whistles, extensive experiments show that PDVC is capable of producing high-quality captioning results, surpassing the state-of-the-art two-stage methods when its localization accuracy is on par with them.
- InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring214
The non-trivial 3D visual grounding task has been effectively re-formulated as a simplified instance-matching problem, considering that instance-level candidates are more rational than the redundant 3D object proposals.
- SmartEdit: Exploring Complex Instruction-Based Image Editing with Multimodal Large Language Models213
A Bidirectional Interaction Module (BIM) is proposed that enables comprehensive bidirectional information interactions between the input image and the MLLM output and demonstrates that a small amount of complex instruction editing data can effectively stimulate SmartEdit’ s editing capabilities for more complex instructions.
- Multi-compound Transformer for Accurate Biomedical Image Segmentation187
A unified transformer network, termed Multi-Compound Transformer (MCTrans), which incorporates rich feature learning and semantic structure mining into a unified framework and performs intra- and inter-scale self-attention, rather than single-scale attention in previous works.
- SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension156
SEED-Bench-2-Plus is introduced, a benchmark specifically designed for evaluating text-rich visual comprehension of Multimodal Large Language Models and a thorough evaluation involving 34 prominent MLLMs is conducted, to emphasize the current limitations of MLLMs in text-rich visual comprehension.
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- Switchable Normalization for Learning-to-Normalize Deep Representation104
Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network, is proposed and will help ease the usage and understand the normalization techniques in deep learning.
- SEED-Bench-2: Benchmarking Multimodal Large Language Models103
This work categorizes the capabilities of MLLMs into hierarchical levels from $L_0 to $L-4 based on the modalities they can accept and generate, and proposes SEED-Bench-2, a comprehensive benchmark that evaluates the hierarchical capabilities ofMLLMs.
- SCAN: Self-and-Collaborative Attention Network for Video Person Re-Identification92
A novel and practical deep architecture for video person re-identification termed self-and-collaborative attention network (SCAN), which adopts the video pairs as the input and outputs their matching scores and can be predicted by the binary classifier.
- AIM 2020 Challenge on Learned Image Signal Processing Pipeline90
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results, defining the state-of-the-art for practical image signal processing pipeline modeling.
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- PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond85
The proposed polar representation can provide a new perspective for designing algorithms to solve single-shot instance segmentation, and is fully convolutional and can be easily embedded into most off-the-shelf detection methods.
- Attentive Crowd Flow Machines75
A unified neural network module, called Attentive Crowd Flow Machine~ (ACFM), which is able to infer the evolution of the crowd flow by learning dynamic representations of temporally-varying data with an attention mechanism, and achieves significant improvements over the state-of-the-art methods.
- SSN: Learning Sparse Switchable Normalization via SparsestMax57
Sparse switchable normalization (SSN) is presented where the importance ratios are constrained to be sparse, and SparsestMax is a sparse version of softmax, which is guaranteed to select only one normalizer for each normalization layer, avoiding redundant computations and improving interpretability of normalizer selection.
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- Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration49
A simple yet effective Spatial Calibration Module (SCM) is introduced for accurate WSOL, incorporating semantic similarities of patch tokens and their spatial relationships into a unified diffusion model, and introduces a learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation.
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 2026-10-13. 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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