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Counterfactual Debiasing for Fact Verification 579 In this paper, we have proposed a novel counter- factual framework CLEVER for debiasing fact- checking models Unlike existing works, CLEVER is augmentation-free and mitigates biases on infer- ence stage In CLEVER, the claim-evidence fusion model and the claim-only model are independently trained to capture the corresponding information
DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION - OpenReview Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques The first is the disentangled attention mechanism, where each word is
Weakly-Supervised Affordance Grounding Guided by Part-Level. . . In this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and egocentric
FreeVS: Generative View Synthesis on Free Driving Trajectory Novelty Clever use of pseudo-images obtained through colored point cloud projection as a unified representation for all view priors, simplifying the learning objective for the generative model Evaluation Introduces two new challenging benchmarks - novel camera synthesis and novel trajectory synthesis Efficiency The authors claim it takes less computational resources at inference time
Diffusion Generative Modeling for Spatially Resolved Gene. . . Weakness 3 (Novelty) The proposed method seems like a clever application of conditional diffusion models to the problem Can the authors further comment on the novelty of their method and how is it different compared to the existing literature? Thank you for allowing us to further clarify the novelty of Stem compared with existing methods
Semi-supervised Camouflaged Object Detection from Noisy Data 1 The Pixel-level loss reweighting method is more clever, but the integrated learning using two network fusion feature designs is too bloated 2 The paper proposes for the first time the use of semi-supervised learning to solve the problems of noisy labels and difficulty in obtaining labels in Camouflaged Object Detection 3 The paper makes a
KnowTrace: Explicit Knowledge Tracing for Structured. . . " This paper introduces a clever incorporation of knowledge graph operation for structured RAG " (Reviewer ifaQ) " The proposed method is straightforward, intuitive, and easy to implement "; " It is innovative that the paper leverages the structured nature of reasoning paths to filter and refine generated trajectories for model training