27 accepted papers
Accurate monocular 3D object detection (M3OD) is pivotal for safety-critical applications like autonomous driving, yet its reliability deteriorates significantly under real-world domain shifts caused by environmental or sensor variations. To address these shifts, Test-Time Adaptation (TTA) methods h…
By locally encoding raw data into intermediate features, collaborative inference enables end users to leverage powerful deep learning models without exposure of sensitive raw data to cloud servers. However, recent studies have revealed that these intermediate features may not sufficiently preserve p…
The remarkable success of "pretrain-then-finetune" paradigm has led to a proliferation of available pre-trained models for vision tasks. This surge presents a significant challenge in efficiently choosing the most suitable pre-trained models for downstream tasks. The critical aspect of this challeng…
Transfer learning has become crucial in computer vision tasks due to the vast availability of pre-trained deep learning models. However, selecting the optimal pre-trained model from a diverse pool for a specific downstream task remains a challenge. Existing methods for measuring the transferability…
Real-world visual search systems involve deployments on multiple platforms with different computing and storage resources. Deploying a unified model that suits the minimal-constrain platforms leads to limited accuracy. It is expected to deploy models with different capacities adapting to the resourc…
Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. Data heterogeneity is one of the main challenges in FL, which results in slow convergence and degraded performance. Most existing approaches only tackle the heterogeneity challenge by restricting the local…
"Image BERT pre-training with masked image modeling (MIM) becomes a popular practice to cope with self-supervised representation learning. A seminal work, BEiT, casts MIM as a classification task with a visual vocabulary, tokenizing the continuous visual signals into discrete vision tokens using a p…
In object re-identification (ReID), the development of deep learning techniques often involves model updates and deployment. It is unbearable to re-embedding and re-index with the system suspended when deploying new models. Therefore, backward-compatible representation is proposed to enable ``new''…
Federated Learning (FL) aims to establish a shared model across decentralized clients under the privacy-preserving constraint. Despite certain success, it is still challenging for FL to deal with non-IID (non-independent and identical distribution) client data, which is a general scenario in real-wo…
Domain generalizable (DG) person re-identification (ReID) is a challenging problem because we cannot access any unseen target domain data during training. Almost all the existing DG ReID methods follow the same pipeline where they use a hybrid dataset from multiple source domains for training, and t…
Unsupervised domain adaptive person re-identification (UDA re-ID) aims at transferring the labeled source domain's knowledge to improve the model's discriminability on the unlabeled target domain. From a novel perspective, we argue that the bridging between the source and target domains can be utili…
Recently, person re-identification (ReID) has vastly benefited from the surging waves of data-driven methods. However, these methods are still not reliable enough for real-world deployments, due to the insufficient generalization capability of the models learned on existing benchmarks that have limi…
In this paper, we consider the absorption effect for the problem of single image reflection removal. We show that the absorption effect can be numerically approximated by the average of refractive amplitude coefficient map. We then reformulate the image formation model and propose a two-step solutio…
Despite great progress in supervised semantic segmentation, a large performance drop is usually observed when deploying the model in the wild. Domain adaptation methods tackle the issue by aligning the source domain and the target domain. However, most existing methods attempt to perform the alignme…
Product Quantization (PQ) is one of the most popular Approximate Nearest Neighbor (ANN) methods for large-scale image retrieval, bringing better performance than hashing based methods. In recent years, several works extend the hard quantization to soft quantization with specially designed deep neura…
Vehicle Re-Identification (ReID) has attracted lots of research efforts due to its great significance to the public security. In vehicle ReID, we aim to learn features that are powerful in discriminating subtle differences between vehicles which are visually similar, and also robust against differen…
Frequency aliasing in the digital capture of display screens leads to the moir´e pattern, appearing as stripe-shaped distortions in images. Efforts to demoir´eing have been made recently in a learning fashion due to the complexity and diversity of the pattern appearance. However, existing methods ca…
For images taken through glass, existing methods focus on the restoration of the background scene by regarding the reflection components as noise. However, the scene reflected by glass surface also contains important information to be recovered, especially for the surveillance or criminal investigat…
This paper aims to calibrate the orientation of glass and the field of view of the camera from a single reflection-contaminated image. We show how a reflective amplitude coefficient map can be used as a calibration cue. Different from existing methods, the proposed solution is free from image conten…
Existing learning-based single image reflection removal methods using paired training data have fundamental limitations about the generalization capability on real-world reflections due to the limited variations in training pairs. In this work, we propose to jointly generate and separate reflections…
Moire pattern is a common image quality degradation caused by frequency aliasing between monitors and cameras when taking screen-shot photos. The complex frequency distribution, imbalanced magnitude in colour channels, and diverse appearance attributes of moire pattern make its removal a challenging…
This paper solves the Sparse Photometric stereo through Lighting Interpolation and Normal Estimation using a generative Network (SPLINE-Net). SPLINE-Net contains a lighting interpolation network to generate dense lighting observations given a sparse set of lights as inputs followed by a normal estim…
Deep image embedding aims at learning a convolutional neural network (CNN) based mapping function that maps an image to a feature vector. The embedding quality is usually evaluated by the performance in image search tasks. Since very few users bother to open the second page search results, top-k pre…
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their l…
In action prediction (early action recognition), the goal is to predict the class label of an ongoing action using its observed part so far. In this paper, we focus on online action prediction in streaming 3D skeleton sequences. A dilated convolutional network is introduced to model the motion dynam…
Removing undesired reflections from a photo taken in front of a glass is of great importance for enhancing the efficiency of visual computing systems. Various approaches have been proposed and shown to be visually plausible on small datasets collected by their authors. A quantitative comparison of e…
Long Short-Term Memory (LSTM) networks have shown superior performance in 3D human action recognition due to their power in modeling the dynamics and dependencies in sequential data. Since not all joints are informative for action analysis and the irrelevant joints often bring a lot of noise, we nee…