513 citations · 1.8k across the 73 of their papers we have counts for
40 papers · 1 filter
The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning
Mingkai Tian, Guorong Li, Yuankai Qi +4
Zero-shot video captioning requires that a model generate high-quality captions without human-annotated video-text pairs for training. State-of-the-art approaches to the problem le…
Categorical Keypoint Positional Embedding for Robust Animal Re-Identification
Yuhao Lin, Lingqiao Liu, Javen Shi
Animal re-identification (ReID) has become an indispensable tool in ecological research, playing a critical role in tracking population dynamics, analyzing behavioral patterns, and…
A Simple-but-effective Baseline for Training-free Class-Agnostic Counting
Yuhao Lin, Haiming Xu, Lingqiao Liu +1
Class-Agnostic Counting (CAC) seeks to accurately count objects in a given image with only a few reference examples. While previous methods achieving this relied on additional trai…
Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines
Hamed Damirchi, Cristian Rodríguez-Opazo, Ehsan Abbasnejad +4
Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box…
CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts
Yichao Cai, Yuhang Liu, Zhen Zhang +1
Contrastive vision-language models, such as CLIP, have garnered considerable attention for various downstream tasks, mainly due to the remarkable ability of the learned features fo…
Influence Function Based Second-Order Channel Pruning-Evaluating True Loss Changes For Pruning Is Possible Without Retraining
Hongrong Cheng, Miao Zhang, Javen Qinfeng Shi
A challenge of channel pruning is designing efficient and effective criteria to select channels to prune. A widely used criterion is minimal performance degeneration. To accurately…
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation
Yuhao Lin, Haiming Xu, Lingqiao Liu +2
Autoencoding, which aims to reconstruct the input images through a bottleneck latent representation, is one of the classic feature representation learning strategies. It has been s…
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw +85
This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conj…
Implicit Sample Extension for Unsupervised Person Re-Identification
Xinyu Zhang, Dongdong Li, Zhigang Wang +5
Most existing unsupervised person re-identification (Re-ID) methods use clustering to generate pseudo labels for model training. Unfortunately, clustering sometimes mixes different…
Active Learning by Feature Mixing
Amin Parvaneh, Ehsan Abbasnejad, Damien Teney +3
The promise of active learning (AL) is to reduce labelling costs by selecting the most valuable examples to annotate from a pool of unlabelled data. Identifying these examples is e…
Learning for Visual Navigation by Imagining the Success
Mahdi Kazemi Moghaddam, Ehsan Abbasnejad, Qi Wu +2
Visual navigation is often cast as a reinforcement learning (RL) problem. Current methods typically result in a suboptimal policy that learns general obstacle avoidance and search…
Consistency-Aware Graph Network for Human Interaction Understanding
Zhenhua Wang, Jiajun Meng, Dongyan Guo +3
Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU). Apart from the latter task is much m…
Joint Learning of Social Groups, Individuals Action and Sub-group Activities in Videos
Mahsa Ehsanpour, Alireza Abedin, Fatemeh Saleh +3
The state-of-the art solutions for human activity understanding from a video stream formulate the task as a spatio-temporal problem which requires joint localization of all individ…
Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation
Mahdi Kazemi Moghaddam, Qi Wu, Ehsan Abbasnejad +1
We humans can impeccably search for a target object, given its name only, even in an unseen environment. We argue that this ability is largely due to three main reasons: the incorp…
MOT20: A benchmark for multi object tracking in crowded scenes
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan +6
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide…
Learn to Predict Sets Using Feed-Forward Neural Networks
Hamid Rezatofighi, Tianyu Zhu, Roman Kaskman +6
This paper addresses the task of set prediction using deep feed-forward neural networks. A set is a collection of elements which is invariant under permutation and the size of a se…
Gradient Information Guided Deraining with A Novel Network and Adversarial Training
Yinglong Wang, Haokui Zhang, Yu Liu +2
In recent years, deep learning based methods have made significant progress in rain-removing. However, the existing methods usually do not have good generalization ability, which l…
Follow the Attention: Combining Partial Pose and Object Motion for Fine-Grained Action Detection
Mohammad Mahdi Kazemi Moghaddam, Ehsan Abbasnejad, Javen Shi
Retailers have long been searching for ways to effectively understand their customers' behaviour in order to provide a smooth and pleasant shopping experience that attracts more cu…
Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes
Yinglong Wang, Qinfeng Shi, Ehsan Abbasnejad +3
Rain removal in images/videos is still an important task in computer vision field and attracting attentions of more and more people. Traditional methods always utilize some incompl…
CVPR19 Tracking and Detection Challenge: How crowded can it get?
Patrick Dendorfer, Hamid Rezatofighi, Anton Milan +6
Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide…
An Effective Two-Branch Model-Based Deep Network for Single Image Deraining
Yinglong Wang, Dong Gong, Jie Yang +4
Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing. Conventional methods rely on some heuri…
Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead
Amin Parvaneh, Ehsan Abbasnejad, Qi Wu +2
Negotiation, as an essential and complicated aspect of online shopping, is still challenging for an intelligent agent. To that end, we propose the Price Negotiator, a modular deep…
RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion
Jie Li, Yu Liu, Dong Gong +4
RGB images differentiate from depth images as they carry more details about the color and texture information, which can be utilized as a vital complementary to depth for boosting…
Attention-guided Network for Ghost-free High Dynamic Range Imaging
Qingsen Yan, Dong Gong, Qinfeng Shi +4
Ghosting artifacts caused by moving objects or misalignments is a key challenge in high dynamic range (HDR) imaging for dynamic scenes. Previous methods first register the input lo…