Bmvc 2023 proceedings. Event: British Machine Vision Conference (BMVC) 2023.

That is, the positive and negative pairs of data points become less distinguishable from each other in the hash space. Workshops End Everyone. ing. We introduce two novel loss objectives for GSN: a novel attendance loss and a binding loss. In this work, we focus on taking advantage of the facial cues, beyond the lip region, for robust Audio-Visual Speech Enhancement (AVSE). Video frame interpolation (VFI) is a challenging task that aims to generate intermediate frames between two consecutive frames in a video. Recent works have proposed various pipelines powered by the entanglement of diffusion models and neural fields. 2 days ago · BMVA Press 2023 [contents] 34th British Machine Vision Conference Workshop Proceedings, BMVC Workshop 2023, Aberdeen, UK, November 20-24, 2023. CoordGate allows for selective amplification or attenuation of filters based on their spatial position, effectively acting like a locally This core construct has inspired several concept-based few-shot learning approaches. Submitted papers will be refereed on their Abstract. Neural implicit modeling permits to achieve impressive 3D reconstruction results on small objects, while it exhibits significant limitations in large indoor scenes. Experimental results show that our efficient SNN can achieve 118× speedup on GPU with only 1. Specifically, we adaptively add pseudo labels and pick samples from the query set, then re-train the model using Our extensive quantitative evaluation shows that our approach significantly improves the performance of the network without adaptation. edu. Farrukh Rahman. @inproceedings{Delitzas_2023_BMVC, author = {Alexandros Delitzas and Maria Parelli and Nikolas Hars and Georgios Vlassis and Sotirios-Konstantinos Anagnostidis and Gregor Bachmann and Thomas Hofmann}, title = {Multi-CLIP: Contrastive Vision-Language Pre-training for Question Answering tasks in 3D Scenes}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK We used this workflow to create the SynBlink dataset, which includes 50,000 video clips and their corresponding annotations. unimore. : Online 22nd - 25th November 2021. of Computer Science, Durham University, South Road, Durham, DH1 3LE, UK). We jointly train CNN and Transformer models, regularising their features to be semantically consistent across different scales. We introduce an end-to-end differentiable foveated active vision architecture that leverages a graph convolutional network to process foveated images, and a simple yet effective formulation for foveated image sampling. We reformulate the lip reading task with uni-modal data into two sub-tasks: learning linguistic priors from uni-modal texts and learning to @inproceedings{Nguyen_2023_BMVC, author = {Quang Vinh Nguyen and Van Thong Huynh and Soo-Hyung Kim}, title = {Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year = {2023}, url = {https To address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features. ReCoT uses a two-head network in each view, with one for clean data modeling (clean net) and the other for noisy data @inproceedings{Yazdani_2023_BMVC, author = {Amirsaeed Yazdani and Xuelu Li and Vishal Monga}, title = {Maturity-Aware Active Learning for Semantic Segmentation with Hierarchically-Adaptive Sample Assessment}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year In our evaluation using the nuScenes dataset, our algorithm effectively counters various LiDAR spoofing attacks, achieving a low (< 10%) false positive ratio and high (> 85%) true positive ratio, outperforming existing state-of-the-art defense methods, CARLO and 3D-TC2. jhu. In this work, we present a unique and systematic study of a temporal bias due to frame length discrepancy between training and test sets of trimmed video clips, which is the first such attempt for a text-video retrieval task, to the best of our knowledge. It is one of the major international conferences on computer vision and related areas held Abstract. To achieve the facial expression captioning model, we propose a three-stage training @inproceedings{An_2023_BMVC, author = {Zeyu An and Changjian Deng and Wanli Dang andThe Second Research Institute of the Civil Aviation Administration of Chin and Zhicheng Dong and 谦 罗 and Jian Cheng}, title = {FLRKD: Relational Knowledge Distillation Based on Channel-wise Feature Quality Assessment}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK In this paper, we focus on representation learning for semi-supervised learning, by developing a novel Multi-Scale Cross Supervised Contrastive Learning (MCSC) framework, to segment structures in medical images. Notably, while the process operates within a 2D image @inproceedings{sundingkai_2023_BMVC, author = {Su sundingkai and Mengqiu Xu and Kaixin Chen and Ming Wu and Chuang Zhang}, title = {E2SAM: A Pipeline for Efficiently Extending SAM's Capability on Cross-Modality Data via Knowledge Inheritance}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year = {2023}, url To address this gap, we propose a novel approach called RepQ, which applies quantization to re-parametrized networks. Website: https://ailb-web. The model not only detects blinks for the entire @inproceedings{Gloudemans_2023_BMVC, author = {Derek Gloudemans and Daniel Work and Yanbing Wang and Gracie E Gumm and William Barbour}, title = {The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year = {2023}, url = {https://papers The proposed method can be easily used to improve existing photometric stereo methods for better estimation of shadow estimation results. Cross-domain few-shot segmentation (CD-FSS) aims to achieve semantic segmentation in previously unseen domains with a limited number of annotated samples. While RGB images provide abundant color information and are easily understood by humans, they also add extra storage and computational burden for neural networks. In the first stage of CL-ILE, we introduce identity-label embeddings (ILEs) to train an ID feature encoder capable of generating person-specific feature embeddings for input face images. We contribute to both the embedding and aggregation steps. Point-level supervised temporal action localization (PTAL) aims at recognizing and localizing actions in untrimmed videos where only a single point (frame) within every action instance is annotated in training data. The field has witnessed promising results achieved by pioneering Inspired by the fact that DNNs are over-parameterized for superior performance, we propose diversifying the high-level features (DHF) for more transferable adversarial examples. In addition, we further improve the performance with our proposed higher-order derivation loss configuration. The British Machine Vision Conference (BMVC) is the British Machine Vision Association’s (BMVA) annual conference on machine vision, image processing, and pattern recognition. We make the following contributions: (i) we initiate The 32. To alleviate this problem, in this paper a novel Similarity Distribution Calibration (SDC) method is introduced. The British Machine Vision Conference (BMVC) is the British Machine Vision Association (BMVA) annual conference on machine vision, image processing, and pattern recognition. However, most existing methods lack explicit semantics or require strong supervision to impose semantic structure over their concept representations. Existing zero-shot captioning methods use token-level optimization that drives the generation of each Unsupervised Domain Adaptation (UDA), a process by which a model trained on a well-annotated source dataset is adapted to an unlabeled target dataset, has emerged as a promising solution for deploying semantic segmentation models in scenarios where annotating extensive amounts of data is cost-prohibitive. In this work, we propose McQueen, a mixed precision quantization technique for early exit networks. Abstract. Our model outperforms state-of-the-art supervised models by a large margin, highlighting the Experimental results show that our proposed method achieves state-of-the-art performance on the joint LLE & SR task in both within-dataset and cross-dataset settings. To overcome these limitations, we propose a cascade sparse feature propagation network that selects the cleaned user-provided point information and propagates user-provided information to unlabeled regions. In this paper we propose CoordGate, a novel lightweight module that uses a multiplicative gate and a coordinate encoding network to enable efficient computation of spatially-varying convolutions in CNNs. Travel Valid Period: November 13, 2023 to December 1, 2023. On the other hand, raw Bayer images preserve primitive color @inproceedings{Kumar_2023_BMVC, author = {Prashant Kumar and Dheeraj Vattikonda and Vedang Bhupesh Shenvi Nadkarni and Erqun Dong and Sabyasachi Sahoo}, title = {Differentiable SLAM Helps Deep Learning-based LiDAR Perception Tasks}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year = {2023}, url = {https Conference Schedule. In this work, we propose a weakly-supervised and visually grounded concept learner (VGCoL), which enforces The similarity range is bounded by the code length and can lead to a problem known as similarity collapse. Image completion techniques have made significant progress in filling missing regions (i. Our proposed semi-adversarial framework processes an input video by adding unobtrusive perturbations that We present an unsupervised data-driven approach for non-rigid shape matching. We propose a novel objective that learns Our proposed approach, referred to as SketchDreamer, integrates a differentiable rasteriser of Bézier curves that optimises an initial input to distil abstract semantic knowledge from a pretrained diffusion model. Our local in-person meeting will be held at P&J Live, Aberdeen, UK. We utilise Score Distillation Sampling to learn a sketch that aligns with a given caption, which importantly enable both text and Transfer learning (TL) from pretrained deep models is a standard practice in modern medical image classification (MIC). The website for the 34th British Machine Vision Conference. However, detecting landmarks in challenging settings, such as head pose changes, exaggerated expressions, or uneven illumination, continue to remain a challenge due to high variability and insufficient samples. e. Next, we invert the input image to latent noise and obtain optimized null text embeddings. Weak Supervision for Label Efficient Visual Bug Detection. Recent advances propose deep fully unsupervised image retrieval aiming at training a deep model from scratch to jointly optimize visual features and quantization codes with minimal human supervision. -1. Further, we propose a gradient masking technique that facilitates the joint optimization of To address this issue, we propose mmPoint, the first model capable of generating dense human point clouds from mmWave radar signals. Existing learning-based VFI methods have achieved great success, but they still suffer from limited generalization ability due to the limited motion distribution of training datasets. Recently proposed ViT-based segmentation approaches employ a Transformer backbone and exploit self-attentive features as an May 5, 2023 · BMVC 2023 organizers will collect workshop registrations, provide facilities, and distribute electronic copies of the workshop proceedings. In addition, we introduce a pipeline called Gap Filler (GaFi), which applies these techniques in an optimal and coordinated manner to maximise classification Abstract. We enable quantization-aware training by applying @inproceedings{Maxwell_2023_BMVC, author = {Bruce A Maxwell and Sumegha Singhania and Heather Fryling and Haonan Sun}, title = {Log RGB Images Provide Invariance to Intensity and Color Balance Variation for Convolutional Networks}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year = {2023}, url = {https://papers Air France & KLM. We explore the power of pretrained 2D diffusion models and standard 3D neural radiance fields as independent, standalone tools and demonstrate Coarse-grained labels, on the other hand, are easily accessible, which makes WSI classification an ideal use case for multiple instance learning (MIL). In particular, DHF perturbs the high-level features by randomly transforming the high-level features and mixing them with the feature of benign samples when calculating The 34th BMVC will now be an in-person event from 20th—24th November 2023. Workshops Start Everyone. Shape matching identifies correspondences between two shapes and is a fundamental step in many computer vision and graphics applications. 1. Our approach is designed to be particularly robust when matching shapes digitized using 3D scanners that contain fine geometric @inproceedings{Schmidt_2023_BMVC, author = {Sebastian Schmidt and Stephan Günnemann}, title = {Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year = {2023 The PEIM is trained to generate prototypes from few-shot normal samples to give priors and further uses them to guide the student to restore distillation targets. Nov 23, 2023 · The Third Workshop on Computational Aspects of Deep Learning (CADL 2023) Thursday, 23rd November 2023. These challenges result in specialized neural network architectures tailored for SITS analysis. To extract prior knowledge of the background, we construct a knowledge graph with information extracted from the image and generate a relevance score matrix (RS) for prior knowledge and the camouflaged object with GCN as the To address the challenges posed by complex prompts or scenarios involving multiple entities and to achieve improved attribute binding, we propose Divide & Bind. We utilize the adversarial loss previously employed in domain adaptation to align feature distributions between source and target domains, to enhance feature robustness and In this paper, we propose BiUNet, a powerful and efficient model which well incorporates a lightweight attention module, Bi-Level Routing Attention (BRA). 2543446, and a non-profit-making body, registered in England and Wales as Charity No. To assess the effectiveness of our method, we conduct comprehensive experiments and compare our Depth completion aims at increasing the resolution of such a depth image by infilling and interpolating the sparse depth values. To demonstrate pixel-level infilling results, a dedicated bi-directional fusion of the warping results is applied. It injects a small number of poisoned images with the correct label into the We present a general methodology that learns to classify images without labels by leveraging pretrained feature extractors. 1002307 (Registered Office: Dept. Besides, to compensate for the information loss caused by downsampling and further enhance the network’s performance, we introduce two innovative techniques termed pixel merging and pixel @inproceedings{Kwon_2023_BMVC, author = {Gitaek Kwon and Jaeyoung Kim and Hong-Jun Choi and Byung-Moo Yoon and Sungchul Choi and Kyu-Hwan Jung}, title = {Improving Out-of-Distribution Detection Performance using Synthetic Outlier Exposure Generated by Visual Foundation Models}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher Our objective is open-world object counting in images, where the target object class is specified by a text description. The proposed model consists of a novel @inproceedings{Li_2023_BMVC, author = {Dongqi Li and Zhu Teng and Li Qirui and Wang Ziyin and Baopeng Zhang and Jianping Fan}, title = {Learning Disentangled Representations for Environment Inference in Out-of-distribution Generalization}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year = {2023}, url = {https Jun 7, 2024 · 34th British Machine Vision Conference Workshop Proceedings, BMVC Workshop 2023, Aberdeen, UK, November 20-24, 2023. Like most existing approaches, we make use of camera images as guidance in very sparse or occluded regions. We first hypothesise and verify the bias on how it would affect the model illustrated with Abstract. Please visit the event @inproceedings{ROH_2023_BMVC, author = {WONSEOK ROH and Seung Hyun Lee and Won Jeong Ryoo and Jakyung Lee and Gyeongrok Oh and Sooyeon Hwang and Hyung-gun Chi and Sangpil Kim}, title = {Functional Hand Type Prior for 3D Hand Pose Estimation and Action Recognition from Egocentric View Monocular Videos}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK In this paper we propose the first learning based method for facial video de-identification that preserves the rPPG signal and visual appearance, thus keeping the utility of the data for remote rPPG measure while protecting users' privacy. This insight has partly motivated the recent differential TL strategies, such as Third, we propose a continuous inference scheme by using a Feed-Forward Integrate-and-Fire (FewdIF) neuron to realize high-speed object detection. We propose \modelnamelong, a framework for narrative video QA that first summarizes the narrative of the video to a short plot and then searches parts of the video relevant to the question. Satellite Image Time Series (SITS) representation learning is complex due to high spatiotemporal resolutions, irregular acquisition times, and intricate spatiotemporal interactions. Event Location: Aberdeen, United Kingdom. Specifically, we develop a Parametric Differentiable Quantizer (PDQ) which learns the quantizer precision, threshold, and scaling factor during training. Since all-purpose visual feature representations are Eric Arazo, Diego Ortego, Paul Albert, Noel O'Connor and Kevin McGuinness Paper Code Oral Session 8 Poster Session 4. In this paper, we propose a few-shot anomaly detection method that integrates adversarial training loss to obtain more robust and generalized feature representations. To this end, we propose the confusing perturbations-induced backdoor attack (CIBA). Extensive experiments conducted on a commonly used AU benchmark dataset, BP4D, show the superiority of EventFormer under . In this paper, we address this problem by incorporating explicit structural guidance with a structure-guided diffusion model (SGDM). % However, besides the above speech-related attributes, there also exist In this paper, we propose a 3D structure-guided tooth alignment network that takes 2D photographs as input (e. Since the method provides appropriate supervision for each unknown viewpoint by the interpolated features, the volume representation is learned better than DietNeRF. , photos captured by smartphones) and aligns the teeth within the 2D image space to generate an orthodontic comparison photograph featuring aesthetically pleasing, aligned teeth. However, a fun- damental problem of contrastive The 34 th BMVC Workshop Proceedings. In this paper, we formalize the problem as using point The British Machine Vision Conference is organised by The British Machine Vision Association and Society for Pattern Recognition. The sparse design of our network enables efficient information propagation on high-resolution features, resulting in more detailed object We propose a novel way, namely UniLip, to utilize uni-modal texts and uni-modal talking face videos for lip reading. First, we specialize a pre-trained diffusion model for the task of face age editing by using an age-aware fine-tuning scheme. The 34th British Machine Vision Conference 20th - 24th November 2023, Aberdeen, UK. Without temporal annotations, most previous works adopt the multiple instance learning (MIL) framework, where the input However, lifting the rich generative priors of these 2D models into 3D is challenging. The quantitative and qualitative analyses demonstrate Abstract. BMVA Press 2023. However, predicting where humans look in 360 scenes presents unique challenges, including spherical distortion, high resolution, and limited labelled data. Additionally, we present BlinkFormer, an innovative blink detection algorithm based on Transformer architecture that fully exploits temporal information from video clips. In our work, we propose a novel embedding-based Dual-Query MIL pipeline (DQ-MIL). Recently, significant breakthrough has been made in multi-view 3D detection tasks. With only uni-modal data, we achieve totally unsupervised lip reading for the first time. Event: British Machine Vision Conference (BMVC) 2023. It also outperforms the state-of-the-art algorithm, especially in more severe conditions, including domain shift and noise. Unlike rotated boxes with fine granularity, point-level annotations only provide a single point for each object as supervision, greatly reducing the annotation burden. @inproceedings{Peng_2023_BMVC, author = {ShengYun Peng and Weilin Xu and Cory Cornelius and Matthew Hull and Kevin Li and Rahul Duggal and Mansi Phute and Jason Martin and Duen Horng Chau}, title = {Robust Principles: Architectural Design Principles for Adversarially Robust CNNs}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023 Typical technique in knowledge distillation (KD) is regularizing the learning of a limited capacity model (student) by pushing its responses to match a powerful model's (teacher). Finally, we perform text-guided local age editing via attention control. In addition, we propose a temporal algorithm that utilizes information from previous timesteps using recurrence. However, large-hole completion remains challenging due to limited structural information. Towards General Game Representations: Decomposing Games Pixels into Content and Style. It is one of the major international conferences on computer vision and related areas held in Partial Person Re-identification (Partial ReID) is a challenging task which aims to match partially visible images with holistic images of the same pedestrian. Semantic segmentation methods are typically designed for RGB color images, which are interpolated from raw Bayer images. BMVC Workshop Proceedings. In this paper, we consider the problem of composed image retrieval (CIR), with the goal of developing models that can understand and combine multi-modal information, e. Knoll}, title = {BEA: Revisiting anchor-based object detection DNN using Budding Ensemble Architecture}, booktitle = {34th British Machine In this paper, we propose an Environmental Knowledge-guided Multi-step Network (EKNet) to simulate this mechanism. Attractive discounts, up to -15%, on a wide range of public fares on all AIR FRANCE, KLM and their code-shared flights worldwide. 21. Our proposed solution can convert low-resolution low-light images into high-resolution images with satisfactory brightness, vivid colors, and more details. Thus approach mainly focuses on instance contrastive learning without using semantic information. , text and images, to accurately retrieve images that match the query, extending the user’s expression ability. In this work, we propose a novel neural implicit modeling method that leverages multiple regularization strategies to achieve better reconstructions of large indoor To improve the quality and diversity of the synthetic dataset, we propose three novel post-processing techniques: Dynamic Sample Filtering, Dynamic Dataset Recycle, and Expansion Trick. In the second stage, we present a data-driven method that implicitly models the relationships among AUs using contrastive loss in a supervised setting while The Rotated Bounding Boxes used in Oriented object detection are labor-intensive and time-consuming to annotate manually. The facial region covers the lip region and furthermore reflects more speech-related attributes obviously, which is beneficial for AVSE. The growing interest in omnidirectional videos (ODVs) that capture the full field-of-view (FOV) has elevated the importance of 360 saliency prediction in computer vision. CounTX is able to count the number of instances of any How Can Contrastive Pre-training Benefit Audio-Visual Segmentation? A Study from Supervised and Zero-shot Perspectives Jiarui Yu (USTC),* Haoran Li (University of Science and Technology of China), Yanbin Hao (University of Science and Technology of China), Wu Jinmeng (Wuhan Institute of Technology), Tong Xu (University of Science and Technology of China), Shuo Wang (University of Science and This paper introduces the Regularized Co-Training (ReCoT) method, which leverages the useful information from both accurately labeled (clean) and inaccurately labeled (noisy) face images to achieve robust AU recognition. The end result is a shared embedding space for the three modalities, which enables the construction of soundscape maps for any geographic region from textual or audio queries. November 2022, London, UK. Moreover, ADoPT shows promising potential for accurate defense in diverse Abstract. British Machine Vision Conference. Weakly-supervised semantic segmentation (WSSS) aims to obtain pixel-wise pseudo labels from image-level labels for segmentation supervision. We further verify our SNN on FPGA platform and the proposed Detecting 3D objects from multi-view images is a fundamental problem in 3D computer vision. 450. Authors are invited to submit full-length high-quality papers in image processing, computer vision, machine learning and related areas for BMVC 2023. The website for the 32nd British Machine Vision Conference, 22nd - 25th November 2021. We also propose to enhance visual matching with LongStoryShort. - 24. @inproceedings{Lê_2023_BMVC, author = {Hoàng-Ân Lê and Minh-Tan Pham}, title = {Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year = {2023}, url Abstract. This paper presents a novel task of describing human facial expressions of a facial image in natural language, which captures the nuances of facial actions and emotional states beyond traditional emotion categories or facial action units (AUs). Neural Style Transfer for Computer Games. it/cadl2023/ Organisers: Giuseppe Fiameni (NVIDIA), Iuri Frosio (NVIDIA), Claudio Baecchi (Small Pixels; University of Florence), Frederic Pariente (NVIDIA), Lorenzo Baraldi (University of Modena and Reggio Emilia) Abstract. The visual feature augmentation module explicitly learns attribute features and employs cosine distance to separate them, thus enhancing attribute Monday, 20 November 2023. Concretely, we propose a smart street parking sign text recognition method that utilizes a large synthetic data and a small real parking sign text data. , holes) in images. Friday, 24 November 2023. The proposed methods, dubbed Generative Pseudo-label based MAML (GP-MAML), GP-ANIL and GP-BOIL (when combined with MAML, ANIL and GP-BOIL respectively), leverage statistics of the query set to improve the performance on new tasks. Workshops ARIA, VUA, MVEO and AI&CV-NDS, different venues in the city. Using the SoundingEarth dataset, we find that our approach significantly outperforms the existing SOTA, with an improvement of image-to-audio Recall@100 from 0. 256 to 0. We focus on providing a multi-candidates technique built upon one general text recognition To this end, we propose EventFormer for AU event detection, which is the first work directly detecting AU events from a video sequence by viewing AU event detection as a multiple class-specific sets prediction problem. g. In-Person Conference Ends Everyone. Albeit useful especially in the penultimate layer and beyond, its action on student's feature transform is rather implicit, limiting its practice in the intermediate This work bridges the gap between scene text recognition and a smart street curbside parking system. Through experiments conducted on the human action dataset, we demonstrate the effectiveness of our approach in predicting valid and diverse motions between given contexts. Subsequently, we adopt a novel contrastive distillation strategy to robustly distill both normal sample representations and inter-sample relations in the training phase. If there are any mistakes on this page, please do not hesitate to contact yyliu@cs. A backdoored deep hashing model is expected to behave normally on original query images and return the images with the target label when a specific trigger pattern presents. Our approach stands out in its ability to faithfully synthesize desired objects with improved In this paper, we present a novel superpixel-based positional encoding technique that combines Vision Transformer (ViT) features with superpixels priors to improve the performance of semantic segmentation architectures. Doctoral Consort. The Association is a Company limited by guarantee, No. The 34. There will be competition for workshop space, time, and topic coverage. The negative Abstract In this paper, we tackle the challenge of actively attending to visual scenes using a foveated sensor. We introduce a zero-shot video captioning method that employs two frozen networks: the GPT-2 language model to generate sentences and the CLIP to maintain a high average matching score between the generated text and the video frames. Thursday, 23 November 2023. 20th - 24th November 2023, Aberdeen, UK. ID Code: 40544AF. However, due to the co-occurrence of multiple categories in an image, it is difficult to obtain accurate pseudo labels for supervision, leading to the unsatisfactory performances of current methods. Specifically, mmPoint takes a single radar frame of a human as input and generates a dense point cloud that accurately reflects the shape of the detected human as output. 5MB parameters for object detection tasks. However, the unprecedented detection performance of these vision BEV (bird's-eye-view) detection models is accompanied with enormous parameters and computation, which make Abstract. Our approach involves self-distillation training of clustering heads, based on the fact that nearest neighbours in the pretrained feature space are likely to share the same label. Although existing CD-FSS models focus on cross-domain feature transformation, relying exclusively on inter-domain knowledge transfer may lead to the loss of critical intra-domain @inproceedings{LIN_2023_BMVC, author = {SHIH CHIH LIN and Ho Weng Lee and Yu-Shuan Hsieh and Cheng Yu Ho and Shang-Hong Lai}, title = {Masked Attention ConvNeXt Unet with Multi-Synthesis Dynamic Weighting for Anomaly Detection and Localization}, booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023}, publisher = {BMVA}, year = {2023}, url The 33. Deep learning methods have led to significant improvements in the performance on the facial landmark detection (FLD) task. Although the recent development of UDA To avoid this harmful training, we propose ManifoldNeRF, a method for supervising feature vectors at unknown viewpoints using interpolated features from neighboring known viewpoints. Our method is based on the insight that the test stage weights of an arbitrary re-parametrized layer can be presented as a differentiable function of trainable parameters. @inproceedings{Syed_2023_BMVC, author = {Qutub Syed and Neslihan Kose Cihangir and Rafael Rosales and Michael Paulitsch and Korbinian Hagn and Florian R Geissler and Yang Peng and Gereon Hinz and Alois C. However, what levels of features to be reused are problem-dependent, and uniformly finetuning all layers of pretrained models may be suboptimal. One of the significant challenges of this task is scale misalignment between holistic and partial person images, which makes it difficult for models to adapt to the scale gaps of The British Machine Vision Conference is organised by The British Machine Vision Association and Society for Pattern Recognition. To this end, we propose CounTX, a class-agnostic, single-stage model using a transformer decoder counting head on top of pre-trained joint text-image representations. ao tu xy wq lz gn rm ze gk ja