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- GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond2,184
A simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation is created, and this simplified design shares similar structure with Squeeze-Excitation Network (SENet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks.
- End-to-End Semi-Supervised Object Detection with Soft Teacher698
This paper presents an end-to-end semi-supervised object detection approach, in contrast to previous more complex multi-stage methods, and proposes two simple yet effective techniques within this framework: a soft teacher mechanism where the classification loss of each unlabeled bounding box is weighed by the classification score produced by the teacher network.
- EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty686
This paper introduces EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency), a simple yet highly efficient speculative sampling framework that effectively resolves the uncertainty, enabling precise second-to-top-layer feature prediction with minimal overhead.
- Side Adapter Network for Open-Vocabulary Semantic Segmentation487
This paper presents a new framework for open-vocabulary semantic segmentation with the pre-trained vision-language model, named Side Adapter Network (SAN), which significantly outperforms other counterparts, with up to 18 times fewer trainable parameters and 19 times faster inference speed.
- CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation484
A new advanced VLA architecture derived from VLM that has a specialized action module conditioned on VLM output is presented that not only significantly surpasses existing VLAs in task performance and but also exhibits remarkable adaptation to new robots and generalization to unseen objects and backgrounds.
- Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model482
A novel method, detection prompt (DetPro), to learn continuous prompt representations for open-vocabulary object detection based on the pre-trained vision-language model, which outperforms the baseline ViLD in all settings.
- A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-Language Model360
This paper refuses the use of the prevalent one-stage FCN based framework, and advocates a two-stage semantic segmentation framework, with the first stage extracting generalizable mask proposals and the second stage leveraging an image based CLIP model to perform open-vocabulary classification on the masked image crops which are generated in the firststage.
- RAIN: Your Language Models Can Align Themselves without Finetuning189
This work introduces a novel inference method, Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation and use the evaluation results to guide rewind and generation for AI safety.
- Unsupervised Prompt Learning for Vision-Language Models189
This paper presents an unsupervised prompt learning (UPL) approach to avoid prompt engineering while simultaneously improving transfer performance of CLIP-like vision-language models.
- A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation169
This simple baseline surpasses the previous state-of-the-art results on two sign language translation benchmarks, demonstrating the effectiveness of transfer learning and can serve as a solid baseline for future research.
- Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation158
This work argues that the image features extracted at a central point contain limited information for predicting distant keypoints or bounding box boundaries, due to object deformation and scale/orientation variation, and proposes to perform regression from a set of points placed at more advantageous positions to facilitate inference.
- Global Context Networks152
This paper replaces the one-layer transformation function of the non-local block by a two-layer bottleneck, which further reduces the parameter number considerably, and effectively models global context in a lightweight manner, allowing it to be applied at multiple layers of a backbone network to form a global context network (GCNet).
- AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls151
AnyTool, a large language model agent designed to revolutionize the utilization of a vast array of tools in addressing user queries, is introduced, operating under the assumption that a subset of these APIs could potentially resolve the queries.
- Natural Language-Assisted Sign Language Recognition119
The Natural Language-Assisted Sign Language Recognition (NLA-SLR) framework is proposed, which exploits semantic information contained in glosses (sign labels) and introduces a novel backbone, video-keypoint network, which not only models both RGB videos and human body keypoints but also derives knowledge from sign videos of different temporal receptive fields.
- High-Fidelity and Arbitrary Face Editing89
This work proposes a simple yet effective method named HifaFace, which achieves high-fidelity and arbitrary face editing, outperforming other state-of-the-art approaches.
- Dual Path Learning for Domain Adaptation of Semantic Segmentation77
This paper proposes a novel dual path learning (DPL) framework that contains two complementary and interactive single-domain adaptation pipelines aligned in source and target domain respectively and shows superiority of the DPL model over the state-of-the-art methods.
- ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment72
Anisotropic direction loss (ADL) and anisotropic attention module (AAM) for coordinate and heatmap regression, respectively are proposed and integrated into an optimized end-to-end training pipeline named ADNet, which achieves state-of-the-art results on 300W, WFLW and COFW datasets.
- Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition66
This work proposes a more effective pseudo-labeling scheme, called Cross-Model Pseudo-Labeling (CMPL), which introduces a lightweight auxiliary network in addition to the primary backbone, and asks them to predict pseudo-labels for each other, and observes that these two models tend to learn complementary representations from the same video clips.
- TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models64
Distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones are explored, suggesting an alternative way for developing small vision Transformer models by exploring better training methods rather than introducing inductive biases into architectures as in most previous works.
- Revisiting Referring Expression Comprehension Evaluation in the Era of Large Multimodal Models62
Ref-L4 is introduced, a comprehensive REC benchmark, specifically designed to evaluate modern REC models, and several LMMs capable of handling the REC task are evaluated, showing significant accuracy improvements, thus highlighting the impact of benchmark noise.
- CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning61
This work adopts a domain-agnostic sign encoder pre-trained on large-scale sign videos into the target domain via pseudo-labeling, and develops a framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo, which outperforms the pioneering method by large margins on various datasets.
- Improving Continuous Sign Language Recognition with Cross-Lingual Signs59
The feasibility of utilizing multilingual sign language corpora to facilitate monolingual CSLR is explored, and the underlying idea is to identify the cross-lingual signs in one sign language and properly leverage them as auxiliary training data to improve the recognition capability of another.
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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 2026-10-15. 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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