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Works512 from public data
- Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images1,529
An end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image by progressively deforming an ellipsoid, leveraging perceptual features extracted from the input image.
- 1,485
- Two-dimensional materials for next-generation computing technologies1,044
The opportunities, progress and challenges of integrating two-dimensional materials with in-memory computing and transistor-based computing technologies, from the perspective of matrix and logic computing, are discussed.
- Evaluating bag-of-visual-words representations in scene classification962
This study provides an empirical basis for designing visual-word representations that are likely to produce superior classification performance and applies techniques used in text categorization to generate image representations that differ in the dimension, selection, and weighting of visual words.
- The THUMOS challenge on action recognition for videos “in the wild”918
The THUMOS benchmark is described in detail, the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal action detection are presented, and how well methods trained on trimmed videos generalize to untrimmed videos is evaluated.
- Towards optimal bag-of-features for object categorization and semantic video retrieval724
This paper evaluates various factors which govern the performance of Bag-of-features, and proposes a novel soft-weighting method to assess the significance of a visual word to an image and experimentally shows it can consistently offer better performance than other popular weighting methods.
- DSOD: Learning Deeply Supervised Object Detectors from Scratch646
Deeply Supervised Object Detector (DSOD), a framework that can learn object detectors from scratch following the single-shot detection (SSD) framework, and one of the key findings is that deep supervision, enabled by dense layer-wise connections, plays a critical role in learning a good detector.
- WildDeepfake609
WildDeepfake is a small dataset that can be used, in addition to existing datasets, to develop and test the effectiveness of deepfake detectors against real-world deepfakes, and proposes two (eg. 2D and 3D) Attention-based Deepfake Detection Networks (ADDNets) to leverage the attention masks on real/fake faces for improved detection.
- NAIS: Neural Attentive Item Similarity Model for Recommendation569
This work proposes a neural network model named Neural Attentive Item Similarity model (NAIS), which is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems.
- Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification479
This work proposes a hybrid deep learning framework for video classification, which is able to model static spatial information, short-term motion, as well as long-term temporal clues in the videos, and achieves very competitive performance on two popular and challenging benchmarks.
- Pose-Normalized Image Generation for Person Re-identification476
This work proposes a novel deep person image generation model for synthesizing realistic person images conditional on the pose based on a generative adversarial network designed specifically for pose normalization in re-id, thus termed pose-normalization GAN (PN-GAN).
- M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection436
A Multi-modal Multi-scale TRansformer (M2TR), which operates on patches of different sizes to detect local inconsistencies in images at different spatial levels, and learns to detect forgery artifacts in the frequency domain to complement RGB information through a carefully designed cross modality fusion block.
- Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks385
Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed regularized DNN (rDNN) is more suitable for modeling video semantics.
- Consumer video understanding366
A new database, CCV, containing 9,317 web videos over 20 semantic categories, including events like "baseball" and "parade", scenes like "beach", and objects like "cat" is described and released, finding that humans are much better at understanding categories of nonrigid objects such as "cat", while current automatic techniques are relatively close to humans in recognizing categories that have distinctive background scenes or audio patterns.
- AdaViT: Adaptive Vision Transformers for Efficient Image Recognition355
AdaViT is introduced, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition.
- Clean-Label Backdoor Attacks on Video Recognition Models335
This paper proposes the use of a universal adversarial trigger as the backdoor trigger to attack video recognition models, a situation where backdoor attacks are likely to be challenged by the above 4 strict conditions.
- Multi-scale Deep Learning Architectures for Person Re-identification319
A novel multi-scale deep learning model is proposed that is able to learn deep discriminative feature representations at different scales and automatically determine the most suitable scales for matching.
- A Survey on Video Diffusion Models318
This article presents a comprehensive review of video diffusion models in the AIGC era, and presents an overview of research on diffusion models in the video domain, categorizing the work into three key areas: video generation, video editing, and other video understanding tasks.
- Balanced Contrastive Learning for Long-Tailed Visual Recognition311
To correct the optimization behavior of SCL and further improve the performance of long-tailed visual recognition, a novel loss for balanced contrastive learning (BCL) is proposed that satisfies the condition of forming a regular simplex and assists the optimization of cross-entropy.
- 291
- Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation288
Hydra-MDP, a novel paradigm employing multiple teachers in a teacher-student model, which features a multi-head decoder to learn diverse trajectory candidates tailored to various evaluation metrics, achieves the 1-st place in the Navsim challenge.
- Recurrent Fusion Network for Image Captioning286
This paper proposes a novel recurrent fusion network (RFNet) for the image captioning task, which can exploit the interactions among the outputs of the image encoders and generate new compact and informative representations for the decoder.
- BEVT: BERT Pretraining of Video Transformers279
BEVT is introduced which decouples video representation learning into spatial representation learning and temporal dynamics learning and achieves state-of-the-art performance on three challenging video benchmarks where BEVT achieves very promising results.
- 270
- CNN-Based Chinese NER with Lexicon Rethinking265
This work proposes a faster alternative to Chinese NER: a convolutional neural network (CNN)-based method that incorporates lexicons using a rethinking mechanism that can model all the characters and potential words that match the sentence in parallel.
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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