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Claim this profileWorks216 from public data
- 1,320
- Contrastive Adaptation Network for Unsupervised Domain Adaptation1,013
This paper proposes Contrastive Adaptation Network optimizing a new metric which explicitly models the intra- class domain discrepancy and the inter-class domain discrepancy, and designs an alternating update strategy for training CAN in an end-to-end manner.
- 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.
- Cross-domain video concept detection using adaptive svms749
This paper proposes Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset and outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus.
- 480
- A discriminative CNN video representation for event detection464
This paper proposes using a set of latent concept descriptors as the frame descriptor, which enriches visual information while keeping it computationally affordable, in a new state-of-the-art performance in event detection over the largest video datasets.
- DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation416
A novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed, which can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions.
- MAGVIT: Masked Generative Video Transformer413
A 3D tokenizer to quantize a video into spatial-temporal visual tokens and propose an embedding method for masked video token modeling to facilitate multi-task learning are introduced.
- Self-Paced Learning with Diversity405
This work proposes an approach called self-paced learning with diversity (SPLD) which formalizes the preference for both easy and diverse samples into a general regularization term, independent of the learning objective, and thus can be easily generalized into various learning tasks.
- The Eighth Visual Object Tracking VOT2020 Challenge Results350
A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in theVDT challenges.
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- Hidden Two-Stream Convolutional Networks for Action Recognition316
This paper presents a novel CNN architecture that implicitly captures motion information between adjacent frames and directly predicts action classes without explicitly computing optical flow, and significantly outperforms the previous best real-time approaches.
- Beyond Gaussian Pyramid: Multi-skip Feature Stacking for action recognition301
This work proposes a novel feature enhancing technique called Multi-skIp Feature Stacking (MIFS), which stacks features extracted using a family of differential filters parameterized with multiple time skips and encodes shift-invariance into the frequency space and proves that MIFS enhances the learnability of differential-based features exponentially.
- KAT: A Knowledge Augmented Transformer for Vision-and-Language232
This work proposes a - Knowledge Augmented Transformer (KAT) - which achieves a strong state-of-the-art result on the open-domain multimodal task of OK-VQA and integrates implicit and explicit knowledge in an encoder-decoder architecture, while still jointly reasoning over both knowledge sources during answer generation.
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- Uncovering the Temporal Context for Video Question Answering188
An encoder–decoder approach using Recurrent Neural Networks to learn the temporal structures of videos and introduce a dual-channel ranking loss to answer multiple-choice questions is presented.
- ExCL: Extractive Clip Localization Using Natural Language Descriptions186
This work proposes a novel extractive approach that predicts the start and end frames by leveraging cross-modal interactions between the text and video - this removes the need to retrieve and re-rank multiple proposal segments.
- 171
- CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis166
A novel dataset for traffic accidents analysis is presented to resolve the lack of public data for research about automatic spatio-temporal annotations for traffic safety in the roads and to integrate contextual information into conventional Faster R-CNN using Context Mining and Augmented Context Mining to complement the accuracy for small pedestrian detection.
- 157
- Learning Spatial Awareness to Improve Crowd Counting151
A novel architecture called SPatial Awareness Network (SPANet) to incorporate spatial context for crowd counting is presented, and the Maximum Excess over Pixels (MEP) loss is proposed to achieve this by finding the pixel-level subregion with high discrepancy to the ground truth.
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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