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- VITON: An Image-Based Virtual Try-on Network787
An image-based VIirtual Try-On Network (VITON) without using 3D information in any form is presented, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy.
- BlockDrop: Dynamic Inference Paths in Residual Networks554
BlockDrop, an approach that learns to dynamically choose which layers of a deep network to execute during inference so as to best reduce total computation without degrading prediction accuracy, is introduced.
- Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification479
This work proposes a hybrid deep learning framework for video classification, which is able to model static spatial information, short-term motion, as well as long-term temporal clues in the videos, and achieves very competitive performance on two popular and challenging benchmarks.
- M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection436
A Multi-modal Multi-scale TRansformer (M2TR), which operates on patches of different sizes to detect local inconsistencies in images at different spatial levels, and learns to detect forgery artifacts in the frequency domain to complement RGB information through a carefully designed cross modality fusion block.
- Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks385
Through arming the DNN with better capability of harnessing both the feature and the class relationships, the proposed regularized DNN (rDNN) is more suitable for modeling video semantics.
- AdaViT: Adaptive Vision Transformers for Efficient Image Recognition355
AdaViT is introduced, an adaptive computation framework that learns to derive usage policies on which patches, self-attention heads and transformer blocks to use throughout the backbone on a per-input basis, aiming to improve inference efficiency of vision transformers with a minimal drop of accuracy for image recognition.
- Automatic Spatially-Aware Fashion Concept Discovery341
This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites, and decomposes the visual-semantic embedding space into multiple concept-specific subspaces to facilitate structured browsing and attribute-feedback product retrieval.
- Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors334
A systematic study of adversarial attacks on state-of-the-art object detection frameworks, and a detailed study of physical world attacks using printed posters and wearable clothes, to quantify the performance of such attacks with different metrics.
- A Survey on Video Diffusion Models318
This article presents a comprehensive review of video diffusion models in the AIGC era, and presents an overview of research on diffusion models in the video domain, categorizing the work into three key areas: video generation, video editing, and other video understanding tasks.
- BEVT: BERT Pretraining of Video Transformers279
BEVT is introduced which decouples video representation learning into spatial representation learning and temporal dynamics learning and achieves state-of-the-art performance on three challenging video benchmarks where BEVT achieves very promising results.
- DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation276
Dual Channel-wise Alignment Networks (DCAN) are presented, a simple yet effective approach to reduce domain shift at both pixel-level and feature-level in deep neural networks for semantic segmentation.
- AdaFrame: Adaptive Frame Selection for Fast Video Recognition272
It is qualitatively demonstrate learned frame usage can indicate the difficulty of making classification decisions; easier samples need fewer frames while harder ones require more, both at instance-level within the same class and at class-level among different categories.
- ObjectFormer for Image Manipulation Detection and Localization240
This paper proposes ObjectFormer, a method to detect and localize image manipulations that outperforms state-of-the-art tampering detection and localization methods, and uses a set of learnable object prototypes as mid-level representations to model the object-level consistencies among different regions.
- Cross-Domain Contrastive Learning for Unsupervised Domain Adaptation238
This work builds upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets, and introduces a simple yet effective framework CDCL, for domain alignment.
- The Regretful Agent: Heuristic-Aided Navigation Through Progress Estimation208
This paper proposes to use a progress monitor developed in prior work as a learnable heuristic for search, and proposes two modules incorporated into an end-to-end architecture that significantly outperforms current state-of-the-art methods using greedy action selection.
- Multi-Stream Multi-Class Fusion of Deep Networks for Video Classification188
A multi-stream framework that is able to exploit multimodal features that are more comprehensive than those previously attempted and produces significantly better results than the state of the arts on two popular benchmarks, 92.2% on UCF-101 (without using audio) and 84.9% on Columbia Consumer Videos.
- Secrets of RLHF in Large Language Models Part II: Reward Modeling179
This report introduces contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization and employs meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples.
- Towards Transferable Adversarial Attacks on Vision Transformers175
A dual attack framework is introduced, which contains a Pay No Attention (PNA) attack and a PatchOut attack, to improve the transferability of adversarial samples across different ViTs, and it is shown that skipping the gradients of attention during backpropagation can generate adversarial examples with high transferability.
- M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers174
M3DETR is the first approach that unifies multiple point cloud representations, feature scales, as well as models mutual relationships between point clouds simultaneously using transformers, and ranks 1st on Waymo Open Dataset with single frame point cloud input.
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- Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders160
A suite of 256 SAEs, trained on each layer and sublayer of the Llama-3.1-8B-Base model, with 32K and 128K features are introduced and modifications to a state-of-the-art SAE variant, Top-K SAEs, are evaluated across multiple dimensions.
- Exploring Inter-feature and Inter-class Relationships with Deep Neural Networks for Video Classification154
A novel unified framework that jointly learns feature relationships and exploits the class relationships for improved video classification performance is proposed and demonstrates that the proposed framework exhibits superior performance over several state-of-the-art approaches.
- DiffusionAD: Norm-Guided One-Step Denoising Diffusion for Anomaly Detection142
DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network, is introduced and a norm-guided paradigm is proposed to integrate the benefits of multiple noise scales, enhancing the fidelity of reconstructions.
- SVFormer: Semi-supervised Video Transformer for Action Recognition142
SVFormer is introduced, which adopts a steady pseudo-labeling framework (i.e., EMA-Teacher) to cope with unlabeled video samples, and a novel augmentation strategy, Tube Token-Mix, tailored for video data where video clips are mixed via a mask with consistent masked tokens over the temporal axis.
- Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning135
This paper proposes masked video distillation (MVD), a simple yet effective two-stage masked feature modeling framework for video representation learning that demonstrates that video transformers pre-trained with spatial-temporal co-teaching outperform models distilled with a single teacher on a multitude of video datasets.
Publication data from OpenAlex; 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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