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Works217 from public data
- Transformers in Vision: A Survey3,900
This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline with an introduction to fundamental concepts behind the success of Transformers, i.e., self-attention, large-scale pre-training, and bidirectional feature encoding.
- Multi-Stage Progressive Image Restoration2,345
This paper proposes a novel synergistic design that can optimally balance these competing goals in image restoration tasks, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps.
- Towards Open World Object Detection654
This work proposes a novel computer vision problem called ORE: Open World Object Detector, which is based on contrastive clustering and energy based unknown identification, and finds that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting.
- Learning Enriched Features for Fast Image Restoration and Enhancement653
The approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details, and achieves state-of-the-art results for a variety of image processing tasks.
- A Guide to Convolutional Neural Networks for Computer Vision623
This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision, providing a comprehensive introduction to CNNs.
- A Deep Journey into Super-resolution503
A taxonomy for deep learning–based super-resolution networks is introduced that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based, and adversarial designs.
- UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation496
This paper proposes a 3D medical image segmentation approach that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed and introduces a novel efficient paired attention block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention.
- CycleISP: Real Image Restoration via Improved Data Synthesis460
This paper presents a framework that models camera imaging pipeline in forward and reverse directions that allows any number of realistic image pairs for denoising both in RAW and sRGB spaces and achieves the state-of-the-art performance on real camera benchmark datasets.
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- A Self-supervised Approach for Adversarial Robustness385
This paper proposes a self-supervised adversarial training mechanism in the input space that provides significant robustness against the unseen adversarial attacks and can be deployed as a plug-and-play solution to protect a variety of vision systems, as it demonstrates for the case of classification, segmentation and detection.
- OW-DETR: Open-world Detection Transformer294
This work introduces a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection that explicitly encodes multi-scale contextual information, possesses less inductive bias, enables knowledge transfer from known classes to the unknown class and can better discriminate between unknown objects and background.
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- Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning217
This paper proposes a novel TPT method, named DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data, and incorporates augmented data by both conventional method and pre-trained stable diffusion to exploit their respective merits, improving the model’s ability to adapt to unknown new test data.
- PromptIR: Prompting for All-in-One Blind Image Restoration212
This work presents a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation, and uses prompts to encode degradation-specific information, which is then used to dynamically guide the restoration network.
- Striking the Right Balance With Uncertainty199
This paper demonstrates that the Bayesian uncertainty estimates directly correlate with the rarity of classes and the difficulty level of individual samples, and presents a novel framework for uncertainty based class imbalance learning that efficiently utilizes sample and class uncertainty information to learn robust features and more generalizable classifiers.
- iTAML: An Incremental Task-Agnostic Meta-learning Approach176
A novel meta-learning approach that seeks to maintain an equilibrium between all the encountered tasks, ensured by a new meta-update rule which avoids catastrophic forgetting and is task-agnostic.
- Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts169
This work introduces a new Zero-Shot Detection problem setting, which aims at simultaneously recognizing and locating object instances belonging to novel categories without any training examples, and designs an original loss function that achieves synergy between max-margin class separation and semantic space clustering.
- Random Path Selection for Continual Learning168
This paper proposes a random path selection algorithm, called RPS-Net, that progressively chooses optimal paths for the new tasks while encouraging parameter sharing and reuse and proposes a simple controller to dynamically balance the model plasticity.
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- Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks161
This work proposes to class-wise disentangle the intermediate feature representations of deep networks, and forces the features for each class to lie inside a convex polytope that is maximally separated from the polytopes of other classes.
- Spatio-temporal Relation Modeling for Few-shot Action Recognition159
A novel few-shot action recognition framework, STRM, which enhances class-specific feature discrim-inability while simultaneously learning higher-order temporal representations and introduces a query-class similarity classifier on the patch-level enriched features to enhance class- specific feature dis-criminability.
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- Fine-Grained Recognition: Accounting for Subtle Differences between Similar Classes147
The main requisite for fine-grained recognition task is to focus on subtle discriminative details that make the subordinate classes different from each other, and this work proposes to explicitly force the network to find the subtle differences among closely related classes.
- Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning142
This work proposes a novel training mechanism that simultaneously enforces equivariance and invariance to a general set of geometric transformations and shows that even without knowledge distillation this proposed method can outperform current state-of-the-art FSL methods on five popular benchmark datasets.
- Incremental Object Detection via Meta-Learning140
A meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared ensures a seamless information transfer via a meta-learned gradient preconditioning that minimizes forgetting and maximizes knowledge transfer.
- Class-Agnostic Object Detection with Multi-modal Transformer138
This paper advocates that existing methods lack a top-down supervision signal governed by human-understandable semantics and demonstrates that Multi-modal Vision Transformers (MViT) trained with aligned image-text pairs can effectively bridge this gap.
- Remote Sensing Change Detection With Transformers Trained From Scratch129
This work develops an end-to-end CD approach with transformers that is trained from scratch and yet achieves state-of-the-art performance on five benchmarks, and introduces a change-enhanced feature fusion (CEFF) module to fuse the features from input image pairs by performing a per-channel re-weighting.
- Burst Image Restoration and Enhancement129
The central idea is to create a set of pseudo-burst features that combine complimentary information from all the input burst frames to seamlessly exchange information, and the approach initially extracts pre-processed features from each burst frame and matches them using an edge-boosting burst alignment module.
- Regularization of deep neural networks with spectral dropout129
This work introduces a new approach called 'Spectral Dropout' to improve the generalization ability of deep neural networks by eliminating weak and 'noisy' Fourier domain coefficients of the neural network activations, leading to remarkably better results than the current regularization methods.
- Improved Visual-Semantic Alignment for Zero-Shot Object Detection125
An end-to-end deep learning framework underpinned by a novel loss function that handles class-imbalance and seeks to properly align the visual and semantic cues for improved zero-shot learning is proposed.
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- ELGC-Net: Efficient Local–Global Context Aggregation for Remote Sensing Change Detection118
This work proposes an efficient change detection (CD) framework, efficient global and local context aggregation network (ELGC-Net), which leverages rich contextual information to precisely estimate change regions while reducing the model size, and introduces ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
- Self-supervised Video Transformer118
This approach is the first to alleviate the dependency on negative samples or dedicated memory banks in Self-supervised Video Transformer (SVT), and supports slow-fast video processing within a single architecture using dynamically adjusted positional encoding and supports longterm relationship modeling along spatiotemporal dimensions.
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- Secure biometric template generation for multi-factor authentication108
The results prove that the proposed framework does not undermine the discriminating features of genuine and forged signatures and the verification performance is comparable to that of the state-of-the-art benchmark results.
- On Generating Transferable Targeted Perturbations104
A new generative approach for highly transferable targeted perturbations (TTP) that not only aligns the global distributions of source and target images, but also matches the local neighbourhood structure between the two domains.
- Orthogonal Projection Loss104
OPL augments the properties of CE loss and directly enforces inter-class separation alongside intra-class clustering in the feature space through orthogonality constraints on the mini-batch level and offers unique advantages e.g., no additional learnable parameters, does not require careful negative mining and is not sensitive to the batch size.
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- NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results94
The NTIRE 2022 challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images and the aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics.
- Handwriting Transformers90
A novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local style patterns, and is the first to introduce a transformer- based network for styling handwritten text generation.
- Sentence-level Prompts Benefit Composed Image Retrieval89
This work demonstrates that learning an appropriate sentence-level prompt for the relative caption (SPRC) is sufficient for achieving effective composed image retrieval and proposes to leverage pretrained V-L models, e.g., BLIP-2, to generate sentence- level prompts.
- Feature Affinity-Based Pseudo Labeling for Semi-Supervised Person Re-Identification89
This paper proposes a novel feature affinity-based pseudo labeling method with two possible label encodings, and is the first study that employs pseudo-labeling by measuring the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.
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- Visual Affordance and Function Understanding83
In this literature survey, “visual affordances” are focused on and current state-of-the-art approaches for solving relevant problems as well as open problems and research gaps are summarized.
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- Synthesizing the Unseen for Zero-Shot Object Detection75
This work proposes a novel generative model that uses class-semantics to not only generate the features but also to discriminatively separate them, using a unified model, to synthesize visual features for unseen classes, so that the model learns both seen and unseen objects in the visual domain.
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- Discriminative Region-based Multi-Label Zero-Shot Learning70
This work proposes an alternate approach towards region-based discriminability-preserving multi-label zero-shot classification that maintains the spatial resolution to preserve region-level characteristics and utilizes a bi-level attention module (BiAM) to enrich the features by incorporating both region and scene context information.
- Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats64
A novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats.
- AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces61
A large-scale, hierarchical annotated dataset of animal faces, featuring 22.4K faces from 350 diverse species and 21 animal orders across biological taxonomy is introduced, showing its challenging nature, unique attributes and present definite prospects for novel, adaptive, and generalized face-oriented CV algorithms.
- Geometry Driven Semantic Labeling of Indoor Scenes61
A discriminative graphical model which integrates geometrical information from RGBD images in its unary, pairwise and higher order components is presented and an improved geometry estimation scheme is proposed which is robust to erroneous sensor inputs.
- Energy-based Latent Aligner for Incremental Learning59
This work proposes ELI: Energy-based Latent Aligner for Incremental Learning, which first learns an energy manifold for the latent representations such that previous task latents will have low energy and the current task latent values have high energy.
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- Semi-Supervised Learning for Few-Shot Image-to-Image Translation54
This work proposes applying semi-supervised learning via a noise-tolerant pseudo-labeling procedure, and applies a cycle consistency constraint to further exploit the information from unlabeled images, either from the same dataset or external.
- Tensor pooling-driven instance segmentation framework for baggage threat recognition52
This is the first contour instance segmentation framework that leverages multi-scale information to recognize cluttered and concealed contraband data from the colored and grayscale security X-ray imagery.
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- Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning51
This work proposes a novel Visual-Augmented Dynamic Semantic prototype method (termed VADS) to boost the generator to learn accurate semantic-visual mapping by fully exploiting the visual-augmented knowledge into semantic conditions.
- Generative Multi-Label Zero-Shot Learning51
This work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting with a cross-level fusion-based generative approach, which outperforms the state-of-the-art on three zero- shot benchmarks: NUS-WIDE, Open Images and MS COCO.
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- Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition47
Video-FocalNet is based on a spatiotemporal focal modulation architecture that reverses the interaction and aggregation steps of self-attention for better efficiency and performs favorably well against the state-of-the-art transformer-based models for video recognition on five large-scale datasets.
- Spectral-GANs for High-Resolution 3D Point-cloud Generation47
This paper develops a principled approach to synthesize 3D point-clouds using a spectral-domain Generative Adversarial Network (GAN) and proposes a fully differentiable block to transform from the spectral to the spatial domain and back, thereby allowing us to integrate knowledge from well-established spatial models.
- Dispersion based Clustering for Unsupervised Person Re-identification.44
A Dispersion based Clustering (DBC) approach which performs better at discovering the underlying data patterns and can automatically prioritize standalone data points and prevents poor clustering.
- AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results43
This paper introduces the real image Super-Resolution challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020, and gauges the state-of-the-art approaches for real image SR in terms of PSNR and SSIM.
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- A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items39
A novel strategy that extends a conventional encoder-decoder architecture to perform instance-aware segmentation and extract merged instances of contraband items without using any additional subnetwork or an object detector is presented.
- Unsupervised anomaly instance segmentation for baggage threat recognition39
A novel unsupervised anomaly instance segmentation framework that recognizes baggage threats, in X-ray scans, as anomalies without requiring any ground truth labels and achieves competitive performance as compared to the conventional fully supervised methods.
- Unsupervised Primitive Discovery for Improved 3D Generative Modeling38
A novel factorized generative model for 3D shape generation that sequentially transitions from coarse to fine scale shape generation and an unsupervised primitive discovery algorithm based on a higher-order conditional random field model are introduced.
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- How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation30
This study rigorously evaluates GPT-4V's adaptability and generalization capabilities in dynamic environments, benchmarking against prominent models like CLIP, LLaVA, and Gemini and delves into GPT-4V's zero-shot generalization across 13 diverse datasets spanning natural, medical, and molecular domains.
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- Towards Instance-adaptive Inference for Federated Learning27
This paper presents a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework, and resorts to a parameter-efficient fine-tuning method, i.e., scale and shift deep features (SSF) upon a pre-trained model.
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- How Good is Google Bard’s Visual Understanding? An Empirical Study on Open Challenges22
This study focuses on 15 diverse task scenarios encompassing regular, camouflaged, medical, under-water and remote sensing data to comprehensively evaluate Google Bard’s performance, and indicates that Bard still struggles in these vision scenarios.
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- Stylized Adversarial Defense21
This work uses the style and content information of the target sample from another class, alongside its class-boundary information, to create adversarial perturbations, and proposes a max-margin adversarial training approach that minimizes the distance between source image and its adversary and maximizes thedistance between the adversary and the target image.
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- Indoor Scene Understanding in 2.5/3D: A Survey.12
This survey paper provides a comprehensive background to this research topic, followed by popular 3D data representations and a comparative analysis of available datasets, and summarizes the performance metrics used for evaluation in different tasks and a quantitative comparison among the recent state of theart techniques.
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- Text to Image for Multi-Label Image Recognition With Joint Prompt-Adapter Learning9
T2I-PAL offers significant advantages: it eliminates the need for fully semantically annotated training images, thereby reducing the manual annotation workload, and it preserves the intrinsic mode of the CLIP model, allowing for seamless integration with any existing CLIP framework.
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- LawDIS: Language-Window-Based Controllable Dichotomous Image Segmentation8
This work presents LawDIS, a language-window-based controllable dichotomous image segmentation (DIS) framework that produces high-quality object masks and significantly outperforms 11 cutting-edge methods across all metrics.
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- A New Perspective to Boost Performance Fairness For Medical Federated Learning8
Fed-LWR is proposed to improve performance fairness from the perspective of feature shift, a key issue influencing the performance of medical FL systems caused by domain shift, and dynamically perceive the bias of the global model across all hospitals.
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- Distorting Neural Representations to Generate Highly Transferable Adversarial Examples.8
This work proposes a novel approach to create adversarial examples that can broadly fool different networks on multiple tasks, based on the following intuition: "Deep features are highly generalizable and show excellent performance across different tasks, therefore an ideal attack must create maximum distortions in the feature space to realize highly transferable examples".
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- Weakly Supervised Change Detection in a Pair of Images.7
This paper employs a deep neural network with DAG topology to learn patterns of change from image-level labeled training data, and applies a constrained mean-field algorithm to estimate the pixel-level labels, which enables imposing global constraints on the observed foreground probability mass function.
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- International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines2
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- MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning1
A vision-centric agent tuning framework that automatically synthesizes multimodal trajectories, generates step-wise preference pairs, and trains a VLM controller for robust tool-use reasoning, demonstrating scalable and effective multimodal tool use.
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- Guidance Through Surrogate: Toward a Generic Diagnostic Attack1
The effect of label smoothing on AT is analyzed as one of the potential causes of gradient masking, leading to a novel attack dubbed guided projected gradient attack (G-PGA), which can be used as a diagnostic tool to reveal elusive robustness due to gradient masking in adversarial defenses.
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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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